http://www.simulace.info/api.php?action=feedcontributions&feedformat=atom&user=Xvegm00 2026-07-27T16:41:31Z User contributions MediaWiki 1.31.1 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=17253 2019-01-23T10:04:32Z

<p>Xvegm00: </p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;complexity&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0-19-515070-8 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. &lt;ref name=&quot;complexity2&quot;&gt;FROM COMPLEXITY TO LIFE &lt;i&gt;Niels Henrik Gregersen &lt;/i&gt;, Oxford University Press 2004, 256 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> There are two kinds of emergence, strong and weak.&lt;ref name=&quot;masfoundations&quot;&gt;FROM COMPLEXITY TO LIFE &lt;i&gt;Niels Henrik Gregersen &lt;/i&gt;, Oxford University Press 2004, 180 p. ISBN 0-19-515070-8 [cit. 2019-01-8]&lt;/ref&gt; Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”.&lt;ref name=&quot;agents&quot;&gt;Socially intelligent agents: creating relationships &lt;i&gt;Cathérine Tessier, Laurent Chaudron, Heinz-Jürgen Müller &lt;/i&gt;, Springer 2002, 298 p. 9780306469855 [cit. 2019-01-8]&lt;/ref&gt; Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities.&lt;ref name=&quot;systems2&quot;&gt;Multiagent Robotic Systems &lt;i&gt;Jiming Liu, Jianbing Wu &lt;/i&gt;, CRC Press 2002, 310 p. 9781420038835 [cit. 2019-01-8]&lt;/ref&gt; This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.&lt;ref name=&quot;systems&quot;&gt;Multiagent Robotic Systems &lt;i&gt;Jiming Liu, Jianbing Wu &lt;/i&gt;, CRC Press 2002, 156 p. 9781420038835 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> [[File:Algae1-xvegm00.jpg]]<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> ;Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> What the collector does so far is tread randomly between foods. So we need to make it visit all the foods consecutively and with the shortest path.<br /> <br /> We give collectors own list foodList that holds the unvisited foods, we give them own list goalList to hold foods already visited and finally listx as a temporary variable, as we need a list that foods can access<br /> <br /> <br /> to move-collectors<br /> ask collectors [<br /> let listx []<br /> if length goalList = count foods[<br /> set goalList []<br /> ]<br /> ifelse any? foods-on patch-here [<br /> set listx foodList<br /> if length listx = 0[<br /> ask foods [set listx fput self listx]<br /> ]<br /> ;we put visited foods to goalList<br /> if member? goal listx [<br /> set goalList fput goal goalList<br /> ;sorting foods by distance causes the list to fill with all the foods<br /> set listx sort-on [distance myself] foods<br /> ]<br /> let list3 []<br /> ;so we need to deduct the already visited foods from the sorted list<br /> foreach listx [<br /> [x] -&gt;<br /> if not member? x goalList [<br /> set list3 lput x list3<br /> ]<br /> ]<br /> set listx list3<br /> if length listx = 0 [<br /> let x min-one-of foods [distance myself]<br /> set listx fput x listx<br /> ]<br /> ;now we simply take the first goal on the sorted list<br /> set goal first listx<br /> walk-towards-goal<br /> set foodList listx<br /> ] [<br /> walk-towards-goal<br /> ]<br /> ]<br /> end<br /> <br /> This will cause the algae to choose the shortest paths properly.<br /> <br /> [[File:xvegm00-algae2.jpg]]<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=17252 2019-01-23T10:04:13Z

<p>Xvegm00: </p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;complexity&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0-19-515070-8 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. &lt;ref name=&quot;complexity2&quot;&gt;FROM COMPLEXITY TO LIFE &lt;i&gt;Niels Henrik Gregersen &lt;/i&gt;, Oxford University Press 2004, 256 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> There are two kinds of emergence, strong and weak.&lt;ref name=&quot;masfoundations&quot;&gt;FROM COMPLEXITY TO LIFE &lt;i&gt;Niels Henrik Gregersen &lt;/i&gt;, Oxford University Press 2004, 180 p. ISBN 0-19-515070-8 [cit. 2019-01-8]&lt;/ref&gt; Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”.&lt;ref name=&quot;agents&quot;&gt;Socially intelligent agents: creating relationships &lt;i&gt;Cathérine Tessier, Laurent Chaudron, Heinz-Jürgen Müller &lt;/i&gt;, Springer 2002, 298 p. 9780306469855 [cit. 2019-01-8]&lt;/ref&gt; Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities.&lt;ref name=&quot;systems2&quot;&gt;Multiagent Robotic Systems &lt;i&gt;Jiming Liu, Jianbing Wu &lt;/i&gt;, CRC Press 2002, 310 p. 9781420038835 [cit. 2019-01-8]&lt;/ref&gt; This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.&lt;ref name=&quot;systems&quot;&gt;Multiagent Robotic Systems &lt;i&gt;Jiming Liu, Jianbing Wu &lt;/i&gt;, CRC Press 2002, 156 p. 9781420038835 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> [[File:Algae1-xvegm00.jpg]]<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> ;Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> What the collector does so far is tread randomly between foods. So we need to make it visit all the foods consecutively and with the shortest path.<br /> <br /> We give collectors own list foodList that holds the unvisited foods, we give them own list goalList to hold foods already visited and finally listx as a temporary variable, as we need a list that foods can access<br /> <br /> <br /> to move-collectors<br /> ask collectors [<br /> let listx []<br /> if length goalList = count foods[<br /> set goalList []<br /> ]<br /> ifelse any? foods-on patch-here [<br /> set listx foodList<br /> if length listx = 0[<br /> ask foods [set listx fput self listx]<br /> ]<br /> ;we put visited foods to goalList<br /> if member? goal listx [<br /> set goalList fput goal goalList<br /> ;sorting foods by distance causes the list to fill with all the foods<br /> set listx sort-on [distance myself] foods<br /> ]<br /> let list3 []<br /> ;so we need to deduct the already visited foods from the sorted list<br /> foreach listx [<br /> [x] -&gt;<br /> if not member? x goalList [<br /> set list3 lput x list3<br /> ]<br /> ]<br /> set listx list3<br /> if length listx = 0 [<br /> let x min-one-of foods [distance myself]<br /> set listx fput x listx<br /> ]<br /> ;now we simply take the first goal on the sorted list<br /> set goal first listx<br /> walk-towards-goal<br /> set foodList listx<br /> ] [<br /> walk-towards-goal<br /> ]<br /> ]<br /> end<br /> <br /> This will cause the algae to choose the shortest paths properly.<br /> [[File:xvegm00-algae2.jpg]]<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Simulation_of_semi-intelligent_algae&diff=17251 2019-01-23T10:03:04Z

<p>Xvegm00: </p> <hr /> <div>= Introduction =<br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> =Problem definition=<br /> The algae works in two modes: food search and optimisation. The food search part involves the algae spreading in a outward pattern, until it finds a foodsource. When it succeeds, it optimizes its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well.<br /> <br /> === Software ===<br /> <br /> Simulation is created using Netlogo 6.0.2.<br /> <br /> = Model =<br /> <br /> === Turtles ===<br /> The agents in our simulation are three by count.<br /> <br /> ==== Foods ====<br /> Foods are agents that stand for foodbits in a petri dish. They are not found at first, until an algae steps on them by chance and sets their patch color from blue to green. This means that the food is now found. There is an exception however, the middle food is found from the beginning, as an inital food source for the living algae.<br /> <br /> ==== Algae ====<br /> These agents are there to simulate algae spread. They dont simulate the whole area of the algae, that would be too expensive and inconvenient. Instead they stand as spreading tendrils of the algae. They color new patches yellow, which means the actual algae area. Their movement is random, although they only turn in random directions by 40°. This number seems to simulate the outward, radial pattern that the algae uses to find new food. It also creates nice veiny, fractal patterns.<br /> <br /> They are hatched on green spots (the food that has been discovered). They have a limited time to live, whenever they step on a new non-yellow tile, it is lowered. This makes sure that there are no tendrils separated from the main body, just like in reality, separated algae cells would die without a food source.<br /> <br /> ==== Collectors ====<br /> Collectors are agents represented by blue arrows. Their task is to optimize the algae. They do so by travelling from food to food and increasing the popularity variable of each yellow patch they step on.<br /> <br /> == Controls ==<br /> === Setup ===<br /> The setup button prepares the simulation environment. First the foods are distributed at preset coordinates. They are represented by static agents with their patch initially colored blue. Further foods can also be placed manually. Secondly the algae agents are hatched from the initial green food node and start their spread. Thirdly collectors are created at green spots as well.<br /> <br /> === Go ===<br /> <br /> to go<br /> make-collectors n-collectors<br /> make-algae physarums<br /> make-step<br /> move-collectors<br /> decay-popularity<br /> draw-turtles<br /> tick<br /> end<br /> <br /> The simulation runs in ticks. The go button enables the repetition of several steps. First you may notice it creates collectors and algae again. This is enable the user to change the number of the agents and to recreate them in case they die. Then both agents make their move. They always move by one patch. <br /> Decay popularity class lowers the popularity variable of all yellow patches, this makes the unused paths decay. Any patches whose popularity decayed too much are recolored to pcolor 39.<br /> Draw turtles class checkes whether the user wants to show the turtles or not.<br /> <br /> === Draw walls and food ===<br /> These buttons allow the user to draw walls and new foods. The button has to be pressed, then you can draw and then it has to be unpressed again. This is because the simulation is in ticks, thus the simulation has to make a new tick to redraw the simulation. The delete walls button works the same way, except it deletes walls.<br /> <br /> === Popularity decay rate ===<br /> This slider sets the speed at which yellow patches decay. For difficult simulations with walls, it is optimal to set it lower (2-3). In an easy scenario, higher settings make the algae optimize quickly.<br /> <br /> === Algae ttl ===<br /> This sets a time to live for the algae tendril agents. High values allow the algae to spread further. Easy scenarios withouth walls are better set lower, to allow nice contrasting patterns of the algae.<br /> <br /> === N collectors and physarums ===<br /> These set the number of collector and algae agents. Higher settings are optimal for difficult scenarios. Lower settings look better.<br /> <br /> === Draw agents ===<br /> This boolean turns off or on the display of agents.<br /> <br /> = Method =<br /> The algae agents are quick to find new food sources despite their random movement. The fact that they are spawned from the new foodsources soon after they are found ensures that a path is quickly built. Their limited time to live ensures that they do not wander too far from their foodsources and disconnect from the main body of the algae.<br /> <br /> Collectors move in a much more sophisticated way. Their logic is that they represent the flow of nutrients through the body of the algae. This is how Physarum discerns the location of foods. Thus the collectors travel between foods and refresh the popularity of the yellow patches, ensuring that the food efficient tendrils stay alive.<br /> <br /> Collectors use the class best way to to determine the direction of movement.<br /> <br /> to-report best-way-to [ destination ]<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let brown-routes visible-patches with [ pcolor = 39]<br /> let routes-that-take-me-closer visible-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let brown-routes-that-take-me-closer brown-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let x routes-that-take-me-closer with [not member? self brown-routes]<br /> ifelse any? x [<br /> report min-one-of x [ distance self ]<br /> ] [<br /> report destination<br /> ]<br /> <br /> end<br /> <br /> This makes the collectors travel between food nodes in shortest paths. However, this also make them connect the foods all-with-all, which isnt the solution that we want. So, in the class move-collectors we make them choose the closest food out of the foods they haven't yet visited. Netlogo was never designed to work with lists filled with agents, so this is rather complex process.<br /> <br /> =Results=<br /> The random radial spread of the algae was copied appropriately. It shows how Physarum Polycephalum manages to find new food sources.<br /> <br /> [[File:xvegm00-algae1.jpg]]<br /> <br /> Collectors simulate the flow of nutrients and optimize the pathways correctly. They allow for constant optimization even in case of sudden changes to the topology. Given enough time, they deal with walls and adapt.<br /> <br /> [[File:xvegm00-algae2.jpg]]<br /> <br /> =Conclusion=<br /> The behaviour of Physarum Polycephalum can be recreated even inside such a simple simulation environment that is netlogo. It shows how the algae is able to find food even without any meaningful sensory or neural organs, simply through its &quot;programming&quot;. The ability of the agents to find the shortest path can be considered an emergent property of the weak type. However, difficult obstacles can be a problem for the turtles to solve. This is because the pathing algorithm that is used doesn't take into calculation the obstacles. They are crossed through random tries and often cause the collectors to get stuck for some time. Improving on this problem would require a sophisticated pathing algorithm such as A* or Djikstra, which would defeat the purpose of the simulation.<br /> =Code=<br /> [[File:Semestralka.nlogo]]</div>

Xvegm00 http://www.simulace.info/index.php?title=Simulation_of_semi-intelligent_algae&diff=17250 2019-01-23T10:02:35Z

<p>Xvegm00: </p> <hr /> <div>= Introduction =<br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> =Problem definition=<br /> The algae works in two modes: food search and optimisation. The food search part involves the algae spreading in a outward pattern, until it finds a foodsource. When it succeeds, it optimizes its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well.<br /> <br /> === Software ===<br /> <br /> Simulation is created using Netlogo 6.0.2.<br /> <br /> = Model =<br /> <br /> === Turtles ===<br /> The agents in our simulation are three by count.<br /> <br /> ==== Foods ====<br /> Foods are agents that stand for foodbits in a petri dish. They are not found at first, until an algae steps on them by chance and sets their patch color from blue to green. This means that the food is now found. There is an exception however, the middle food is found from the beginning, as an inital food source for the living algae.<br /> <br /> ==== Algae ====<br /> These agents are there to simulate algae spread. They dont simulate the whole area of the algae, that would be too expensive and inconvenient. Instead they stand as spreading tendrils of the algae. They color new patches yellow, which means the actual algae area. Their movement is random, although they only turn in random directions by 40°. This number seems to simulate the outward, radial pattern that the algae uses to find new food. It also creates nice veiny, fractal patterns.<br /> <br /> They are hatched on green spots (the food that has been discovered). They have a limited time to live, whenever they step on a new non-yellow tile, it is lowered. This makes sure that there are no tendrils separated from the main body, just like in reality, separated algae cells would die without a food source.<br /> <br /> ==== Collectors ====<br /> Collectors are agents represented by blue arrows. Their task is to optimize the algae. They do so by travelling from food to food and increasing the popularity variable of each yellow patch they step on.<br /> <br /> == Controls ==<br /> === Setup ===<br /> The setup button prepares the simulation environment. First the foods are distributed at preset coordinates. They are represented by static agents with their patch initially colored blue. Further foods can also be placed manually. Secondly the algae agents are hatched from the initial green food node and start their spread. Thirdly collectors are created at green spots as well.<br /> <br /> === Go ===<br /> <br /> to go<br /> make-collectors n-collectors<br /> make-algae physarums<br /> make-step<br /> move-collectors<br /> decay-popularity<br /> draw-turtles<br /> tick<br /> end<br /> <br /> The simulation runs in ticks. The go button enables the repetition of several steps. First you may notice it creates collectors and algae again. This is enable the user to change the number of the agents and to recreate them in case they die. Then both agents make their move. They always move by one patch. <br /> Decay popularity class lowers the popularity variable of all yellow patches, this makes the unused paths decay. Any patches whose popularity decayed too much are recolored to pcolor 39.<br /> Draw turtles class checkes whether the user wants to show the turtles or not.<br /> <br /> === Draw walls and food ===<br /> These buttons allow the user to draw walls and new foods. The button has to be pressed, then you can draw and then it has to be unpressed again. This is because the simulation is in ticks, thus the simulation has to make a new tick to redraw the simulation. The delete walls button works the same way, except it deletes walls.<br /> <br /> === Popularity decay rate ===<br /> This slider sets the speed at which yellow patches decay. For difficult simulations with walls, it is optimal to set it lower (2-3). In an easy scenario, higher settings make the algae optimize quickly.<br /> <br /> === Algae ttl ===<br /> This sets a time to live for the algae tendril agents. High values allow the algae to spread further. Easy scenarios withouth walls are better set lower, to allow nice contrasting patterns of the algae.<br /> <br /> === N collectors and physarums ===<br /> These set the number of collector and algae agents. Higher settings are optimal for difficult scenarios. Lower settings look better.<br /> <br /> === Draw agents ===<br /> This boolean turns off or on the display of agents.<br /> <br /> = Method =<br /> The algae agents are quick to find new food sources despite their random movement. The fact that they are spawned from the new foodsources soon after they are found ensures that a path is quickly built. Their limited time to live ensures that they do not wander too far from their foodsources and disconnect from the main body of the algae.<br /> <br /> Collectors move in a much more sophisticated way. Their logic is that they represent the flow of nutrients through the body of the algae. This is how Physarum discerns the location of foods. Thus the collectors travel between foods and refresh the popularity of the yellow patches, ensuring that the food efficient tendrils stay alive.<br /> <br /> Collectors use the class best way to to determine the direction of movement.<br /> <br /> to-report best-way-to [ destination ]<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let brown-routes visible-patches with [ pcolor = 39]<br /> let routes-that-take-me-closer visible-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let brown-routes-that-take-me-closer brown-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let x routes-that-take-me-closer with [not member? self brown-routes]<br /> ifelse any? x [<br /> report min-one-of x [ distance self ]<br /> ] [<br /> report destination<br /> ]<br /> <br /> end<br /> <br /> This makes the collectors travel between food nodes in shortest paths. However, this also make them connect the foods all-with-all, which isnt the solution that we want. So, in the class move-collectors we make them choose the closest food out of the foods they haven't yet visited. Netlogo was never designed to work with lists filled with agents, so this is rather complex process.<br /> <br /> =Results=<br /> The random radial spread of the algae was copied appropriately. It shows how Physarum Polycephalum manages to find new food sources.<br /> [[File:xvegm00-algae1.jpg]]<br /> Collectors simulate the flow of nutrients and optimize the pathways correctly. They allow for constant optimization even in case of sudden changes to the topology. Given enough time, they deal with walls and adapt.<br /> [[File:xvegm00-algae2.jpg]]<br /> <br /> =Conclusion=<br /> The behaviour of Physarum Polycephalum can be recreated even inside such a simple simulation environment that is netlogo. It shows how the algae is able to find food even without any meaningful sensory or neural organs, simply through its &quot;programming&quot;. The ability of the agents to find the shortest path can be considered an emergent property of the weak type. However, difficult obstacles can be a problem for the turtles to solve. This is because the pathing algorithm that is used doesn't take into calculation the obstacles. They are crossed through random tries and often cause the collectors to get stuck for some time. Improving on this problem would require a sophisticated pathing algorithm such as A* or Djikstra, which would defeat the purpose of the simulation.<br /> =Code=<br /> [[File:Semestralka.nlogo]]</div>

Xvegm00 http://www.simulace.info/index.php?title=File:Semestralka.nlogo&diff=17249 2019-01-23T10:00:49Z

<p>Xvegm00: </p> <hr /> <div></div>

Xvegm00 http://www.simulace.info/index.php?title=File:Xvegm00-algae2.jpg&diff=17248 2019-01-23T10:00:24Z

<p>Xvegm00: </p> <hr /> <div></div>

Xvegm00 http://www.simulace.info/index.php?title=File:Xvegm00-algae1.jpg&diff=17247 2019-01-23T10:00:03Z

<p>Xvegm00: </p> <hr /> <div></div>

Xvegm00 http://www.simulace.info/index.php?title=Simulation_of_semi-intelligent_algae&diff=16939 2019-01-17T09:46:28Z

<p>Xvegm00: Undo revision 16921 by Xvegm00 (talk)</p> <hr /> <div>= Introduction =<br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> =Problem definition=<br /> The algae works in two modes: food search and optimisation. The food search part involves the algae spreading in a outward pattern, until it finds a foodsource. When it succeeds, it optimizes its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well.<br /> <br /> === Software ===<br /> <br /> Simulation is created using Netlogo 6.0.2.<br /> <br /> = Model =<br /> <br /> === Turtles ===<br /> The agents in our simulation are three by count.<br /> <br /> ==== Foods ====<br /> Foods are agents that stand for foodbits in a petri dish. They are not found at first, until an algae steps on them by chance and sets their patch color from blue to green. This means that the food is now found. There is an exception however, the middle food is found from the beginning, as an inital food source for the living algae.<br /> <br /> ==== Algae ====<br /> These agents are there to simulate algae spread. They dont simulate the whole area of the algae, that would be too expensive and inconvenient. Instead they stand as spreading tendrils of the algae. They color new patches yellow, which means the actual algae area. Their movement is random, although they only turn in random directions by 40°. This number seems to simulate the outward, radial pattern that the algae uses to find new food. It also creates nice veiny, fractal patterns.<br /> <br /> They are hatched on green spots (the food that has been discovered). They have a limited time to live, whenever they step on a new non-yellow tile, it is lowered. This makes sure that there are no tendrils separated from the main body, just like in reality, separated algae cells would die without a food source.<br /> <br /> ==== Collectors ====<br /> Collectors are agents represented by blue arrows. Their task is to optimize the algae. They do so by travelling from food to food and increasing the popularity variable of each yellow patch they step on.<br /> <br /> == Controls ==<br /> === Setup ===<br /> The setup button prepares the simulation environment. First the foods are distributed at preset coordinates. They are represented by static agents with their patch initially colored blue. Further foods can also be placed manually. Secondly the algae agents are hatched from the initial green food node and start their spread. Thirdly collectors are created at green spots as well.<br /> <br /> === Go ===<br /> <br /> to go<br /> make-collectors n-collectors<br /> make-algae physarums<br /> make-step<br /> move-collectors<br /> decay-popularity<br /> draw-turtles<br /> tick<br /> end<br /> <br /> The simulation runs in ticks. The go button enables the repetition of several steps. First you may notice it creates collectors and algae again. This is enable the user to change the number of the agents and to recreate them in case they die. Then both agents make their move. They always move by one patch. <br /> Decay popularity class lowers the popularity variable of all yellow patches, this makes the unused paths decay. Any patches whose popularity decayed too much are recolored to pcolor 39.<br /> Draw turtles class checkes whether the user wants to show the turtles or not.<br /> <br /> === Draw walls and food ===<br /> These buttons allow the user to draw walls and new foods. The button has to be pressed, then you can draw and then it has to be unpressed again. This is because the simulation is in ticks, thus the simulation has to make a new tick to redraw the simulation. The delete walls button works the same way, except it deletes walls.<br /> <br /> === Popularity decay rate ===<br /> This slider sets the speed at which yellow patches decay. For difficult simulations with walls, it is optimal to set it lower (2-3). In an easy scenario, higher settings make the algae optimize quickly.<br /> <br /> === Algae ttl ===<br /> This sets a time to live for the algae tendril agents. High values allow the algae to spread further. Easy scenarios withouth walls are better set lower, to allow nice contrasting patterns of the algae.<br /> <br /> === N collectors and physarums ===<br /> These set the number of collector and algae agents. Higher settings are optimal for difficult scenarios. Lower settings look better.<br /> <br /> === Draw agents ===<br /> This boolean turns off or on the display of agents.<br /> <br /> = Method =<br /> The algae agents are quick to find new food sources despite their random movement. The fact that they are spawned from the new foodsources soon after they are found ensures that a path is quickly built. Their limited time to live ensures that they do not wander too far from their foodsources and disconnect from the main body of the algae.<br /> <br /> Collectors move in a much more sophisticated way. Their logic is that they represent the flow of nutrients through the body of the algae. This is how Physarum discerns the location of foods. Thus the collectors travel between foods and refresh the popularity of the yellow patches, ensuring that the food efficient tendrils stay alive.<br /> <br /> Collectors use the class best way to to determine the direction of movement.<br /> <br /> to-report best-way-to [ destination ]<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let brown-routes visible-patches with [ pcolor = 39]<br /> let routes-that-take-me-closer visible-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let brown-routes-that-take-me-closer brown-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let x routes-that-take-me-closer with [not member? self brown-routes]<br /> ifelse any? x [<br /> report min-one-of x [ distance self ]<br /> ] [<br /> report destination<br /> ]<br /> <br /> end<br /> <br /> This makes the collectors travel between food nodes in shortest paths. However, this also make them connect the foods all-with-all, which isnt the solution that we want. So, in the class move-collectors we make them choose the closest food out of the foods they haven't yet visited. Netlogo was never designed to work with lists filled with agents, so this is rather complex process.<br /> <br /> =Results=<br /> The random radial spread of the algae was copied appropriately. It shows how Physarum Polycephalum manages to find new food sources.<br /> [[File:Algae1-xvegm00.jpg]]<br /> Collectors simulate the flow of nutrients and optimize the pathways correctly. They allow for constant optimization even in case of sudden changes to the topology. Given enough time, they deal with walls and adapt.<br /> [[File:Algae2-xvegm00.jpg]]<br /> <br /> =Conclusion=<br /> The behaviour of Physarum Polycephalum can be recreated even inside such a simple simulation environment that is netlogo. It shows how the algae is able to find food even without any meaningful sensory or neural organs, simply through its &quot;programming&quot;. The ability of the agents to find the shortest path can be considered an emergent property of the weak type. However, difficult obstacles can be a problem for the turtles to solve. This is because the pathing algorithm that is used doesn't take into calculation the obstacles. They are crossed through random tries and often cause the collectors to get stuck for some time. Improving on this problem would require a sophisticated pathing algorithm such as A* or Djikstra, which would defeat the purpose of the simulation.<br /> =Code=<br /> [[File:algae-xvegm00.nlogo]]</div>

Xvegm00 http://www.simulace.info/index.php?title=WS_2018/2019&diff=16938 2019-01-17T09:46:00Z

<p>Xvegm00: </p> <hr /> <div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br /> <br /> ==Simulations==<br /> <br /> --[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br /> <br /> ==Papers==<br /> -- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]~<br /> <br /> -- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems Multi-agent systems]]</div>

Xvegm00 http://www.simulace.info/index.php?title=WS_2018/2019&diff=16937 2019-01-17T09:44:51Z

<p>Xvegm00: </p> <hr /> <div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br /> <br /> ==Simulations==<br /> <br /> --[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]<br /> <br /> ==Papers==<br /> -- [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 20:43, 12 January 2019 (CET) [https://en.wikipedia.org/wiki/Data_flow_diagram Complete redo of DFD wikipedia]<br /> -- [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 10:44, 17 January 2019 (CET) [[http://www.simulace.info/index.php/Multi-agent_systems]]</div>

Xvegm00 http://www.simulace.info/index.php?title=Simulation_of_semi-intelligent_algae&diff=16921 2019-01-09T13:01:18Z

<p>Xvegm00: Blanked the page</p> <hr /> <div></div>

Xvegm00 http://www.simulace.info/index.php?title=Simulation_of_semi-intelligent_algae&diff=16920 2019-01-09T12:59:44Z

<p>Xvegm00: /* Results */</p> <hr /> <div>= Introduction =<br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> =Problem definition=<br /> The algae works in two modes: food search and optimisation. The food search part involves the algae spreading in a outward pattern, until it finds a foodsource. When it succeeds, it optimizes its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well.<br /> <br /> === Software ===<br /> <br /> Simulation is created using Netlogo 6.0.2.<br /> <br /> = Model =<br /> <br /> === Turtles ===<br /> The agents in our simulation are three by count.<br /> <br /> ==== Foods ====<br /> Foods are agents that stand for foodbits in a petri dish. They are not found at first, until an algae steps on them by chance and sets their patch color from blue to green. This means that the food is now found. There is an exception however, the middle food is found from the beginning, as an inital food source for the living algae.<br /> <br /> ==== Algae ====<br /> These agents are there to simulate algae spread. They dont simulate the whole area of the algae, that would be too expensive and inconvenient. Instead they stand as spreading tendrils of the algae. They color new patches yellow, which means the actual algae area. Their movement is random, although they only turn in random directions by 40°. This number seems to simulate the outward, radial pattern that the algae uses to find new food. It also creates nice veiny, fractal patterns.<br /> <br /> They are hatched on green spots (the food that has been discovered). They have a limited time to live, whenever they step on a new non-yellow tile, it is lowered. This makes sure that there are no tendrils separated from the main body, just like in reality, separated algae cells would die without a food source.<br /> <br /> ==== Collectors ====<br /> Collectors are agents represented by blue arrows. Their task is to optimize the algae. They do so by travelling from food to food and increasing the popularity variable of each yellow patch they step on.<br /> <br /> == Controls ==<br /> === Setup ===<br /> The setup button prepares the simulation environment. First the foods are distributed at preset coordinates. They are represented by static agents with their patch initially colored blue. Further foods can also be placed manually. Secondly the algae agents are hatched from the initial green food node and start their spread. Thirdly collectors are created at green spots as well.<br /> <br /> === Go ===<br /> <br /> to go<br /> make-collectors n-collectors<br /> make-algae physarums<br /> make-step<br /> move-collectors<br /> decay-popularity<br /> draw-turtles<br /> tick<br /> end<br /> <br /> The simulation runs in ticks. The go button enables the repetition of several steps. First you may notice it creates collectors and algae again. This is enable the user to change the number of the agents and to recreate them in case they die. Then both agents make their move. They always move by one patch. <br /> Decay popularity class lowers the popularity variable of all yellow patches, this makes the unused paths decay. Any patches whose popularity decayed too much are recolored to pcolor 39.<br /> Draw turtles class checkes whether the user wants to show the turtles or not.<br /> <br /> === Draw walls and food ===<br /> These buttons allow the user to draw walls and new foods. The button has to be pressed, then you can draw and then it has to be unpressed again. This is because the simulation is in ticks, thus the simulation has to make a new tick to redraw the simulation. The delete walls button works the same way, except it deletes walls.<br /> <br /> === Popularity decay rate ===<br /> This slider sets the speed at which yellow patches decay. For difficult simulations with walls, it is optimal to set it lower (2-3). In an easy scenario, higher settings make the algae optimize quickly.<br /> <br /> === Algae ttl ===<br /> This sets a time to live for the algae tendril agents. High values allow the algae to spread further. Easy scenarios withouth walls are better set lower, to allow nice contrasting patterns of the algae.<br /> <br /> === N collectors and physarums ===<br /> These set the number of collector and algae agents. Higher settings are optimal for difficult scenarios. Lower settings look better.<br /> <br /> === Draw agents ===<br /> This boolean turns off or on the display of agents.<br /> <br /> = Method =<br /> The algae agents are quick to find new food sources despite their random movement. The fact that they are spawned from the new foodsources soon after they are found ensures that a path is quickly built. Their limited time to live ensures that they do not wander too far from their foodsources and disconnect from the main body of the algae.<br /> <br /> Collectors move in a much more sophisticated way. Their logic is that they represent the flow of nutrients through the body of the algae. This is how Physarum discerns the location of foods. Thus the collectors travel between foods and refresh the popularity of the yellow patches, ensuring that the food efficient tendrils stay alive.<br /> <br /> Collectors use the class best way to to determine the direction of movement.<br /> <br /> to-report best-way-to [ destination ]<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let brown-routes visible-patches with [ pcolor = 39]<br /> let routes-that-take-me-closer visible-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let brown-routes-that-take-me-closer brown-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let x routes-that-take-me-closer with [not member? self brown-routes]<br /> ifelse any? x [<br /> report min-one-of x [ distance self ]<br /> ] [<br /> report destination<br /> ]<br /> <br /> end<br /> <br /> This makes the collectors travel between food nodes in shortest paths. However, this also make them connect the foods all-with-all, which isnt the solution that we want. So, in the class move-collectors we make them choose the closest food out of the foods they haven't yet visited. Netlogo was never designed to work with lists filled with agents, so this is rather complex process.<br /> <br /> =Results=<br /> The random radial spread of the algae was copied appropriately. It shows how Physarum Polycephalum manages to find new food sources.<br /> [[File:Algae1-xvegm00.jpg]]<br /> Collectors simulate the flow of nutrients and optimize the pathways correctly. They allow for constant optimization even in case of sudden changes to the topology. Given enough time, they deal with walls and adapt.<br /> [[File:Algae2-xvegm00.jpg]]<br /> <br /> =Conclusion=<br /> The behaviour of Physarum Polycephalum can be recreated even inside such a simple simulation environment that is netlogo. It shows how the algae is able to find food even without any meaningful sensory or neural organs, simply through its &quot;programming&quot;. The ability of the agents to find the shortest path can be considered an emergent property of the weak type. However, difficult obstacles can be a problem for the turtles to solve. This is because the pathing algorithm that is used doesn't take into calculation the obstacles. They are crossed through random tries and often cause the collectors to get stuck for some time. Improving on this problem would require a sophisticated pathing algorithm such as A* or Djikstra, which would defeat the purpose of the simulation.<br /> =Code=<br /> [[File:algae-xvegm00.nlogo]]</div>

Xvegm00 http://www.simulace.info/index.php?title=Simulation_of_semi-intelligent_algae&diff=16919 2019-01-09T12:58:20Z

<p>Xvegm00: </p> <hr /> <div>= Introduction =<br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> =Problem definition=<br /> The algae works in two modes: food search and optimisation. The food search part involves the algae spreading in a outward pattern, until it finds a foodsource. When it succeeds, it optimizes its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well.<br /> <br /> === Software ===<br /> <br /> Simulation is created using Netlogo 6.0.2.<br /> <br /> = Model =<br /> <br /> === Turtles ===<br /> The agents in our simulation are three by count.<br /> <br /> ==== Foods ====<br /> Foods are agents that stand for foodbits in a petri dish. They are not found at first, until an algae steps on them by chance and sets their patch color from blue to green. This means that the food is now found. There is an exception however, the middle food is found from the beginning, as an inital food source for the living algae.<br /> <br /> ==== Algae ====<br /> These agents are there to simulate algae spread. They dont simulate the whole area of the algae, that would be too expensive and inconvenient. Instead they stand as spreading tendrils of the algae. They color new patches yellow, which means the actual algae area. Their movement is random, although they only turn in random directions by 40°. This number seems to simulate the outward, radial pattern that the algae uses to find new food. It also creates nice veiny, fractal patterns.<br /> <br /> They are hatched on green spots (the food that has been discovered). They have a limited time to live, whenever they step on a new non-yellow tile, it is lowered. This makes sure that there are no tendrils separated from the main body, just like in reality, separated algae cells would die without a food source.<br /> <br /> ==== Collectors ====<br /> Collectors are agents represented by blue arrows. Their task is to optimize the algae. They do so by travelling from food to food and increasing the popularity variable of each yellow patch they step on.<br /> <br /> == Controls ==<br /> === Setup ===<br /> The setup button prepares the simulation environment. First the foods are distributed at preset coordinates. They are represented by static agents with their patch initially colored blue. Further foods can also be placed manually. Secondly the algae agents are hatched from the initial green food node and start their spread. Thirdly collectors are created at green spots as well.<br /> <br /> === Go ===<br /> <br /> to go<br /> make-collectors n-collectors<br /> make-algae physarums<br /> make-step<br /> move-collectors<br /> decay-popularity<br /> draw-turtles<br /> tick<br /> end<br /> <br /> The simulation runs in ticks. The go button enables the repetition of several steps. First you may notice it creates collectors and algae again. This is enable the user to change the number of the agents and to recreate them in case they die. Then both agents make their move. They always move by one patch. <br /> Decay popularity class lowers the popularity variable of all yellow patches, this makes the unused paths decay. Any patches whose popularity decayed too much are recolored to pcolor 39.<br /> Draw turtles class checkes whether the user wants to show the turtles or not.<br /> <br /> === Draw walls and food ===<br /> These buttons allow the user to draw walls and new foods. The button has to be pressed, then you can draw and then it has to be unpressed again. This is because the simulation is in ticks, thus the simulation has to make a new tick to redraw the simulation. The delete walls button works the same way, except it deletes walls.<br /> <br /> === Popularity decay rate ===<br /> This slider sets the speed at which yellow patches decay. For difficult simulations with walls, it is optimal to set it lower (2-3). In an easy scenario, higher settings make the algae optimize quickly.<br /> <br /> === Algae ttl ===<br /> This sets a time to live for the algae tendril agents. High values allow the algae to spread further. Easy scenarios withouth walls are better set lower, to allow nice contrasting patterns of the algae.<br /> <br /> === N collectors and physarums ===<br /> These set the number of collector and algae agents. Higher settings are optimal for difficult scenarios. Lower settings look better.<br /> <br /> === Draw agents ===<br /> This boolean turns off or on the display of agents.<br /> <br /> = Method =<br /> The algae agents are quick to find new food sources despite their random movement. The fact that they are spawned from the new foodsources soon after they are found ensures that a path is quickly built. Their limited time to live ensures that they do not wander too far from their foodsources and disconnect from the main body of the algae.<br /> <br /> Collectors move in a much more sophisticated way. Their logic is that they represent the flow of nutrients through the body of the algae. This is how Physarum discerns the location of foods. Thus the collectors travel between foods and refresh the popularity of the yellow patches, ensuring that the food efficient tendrils stay alive.<br /> <br /> Collectors use the class best way to to determine the direction of movement.<br /> <br /> to-report best-way-to [ destination ]<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let brown-routes visible-patches with [ pcolor = 39]<br /> let routes-that-take-me-closer visible-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let brown-routes-that-take-me-closer brown-routes with [<br /> distance destination &lt; [ distance destination - 1 ] of myself<br /> ]<br /> let x routes-that-take-me-closer with [not member? self brown-routes]<br /> ifelse any? x [<br /> report min-one-of x [ distance self ]<br /> ] [<br /> report destination<br /> ]<br /> <br /> end<br /> <br /> This makes the collectors travel between food nodes in shortest paths. However, this also make them connect the foods all-with-all, which isnt the solution that we want. So, in the class move-collectors we make them choose the closest food out of the foods they haven't yet visited. Netlogo was never designed to work with lists filled with agents, so this is rather complex process.<br /> <br /> =Results=<br /> The random radial spread of the algae was copied appropriately. It shows how Physarum Polycephalum manages to find new food sources.<br /> [[File:Algae1-xvegm00.jpg]]<br /> Collectors simulate the flow of nutrients and optimize the pathways correctly. They allow for constant optimization even in case of sudden changes to the topology.<br /> [[File:Algae2-xvegm00.jpg]]<br /> <br /> =Conclusion=<br /> The behaviour of Physarum Polycephalum can be recreated even inside such a simple simulation environment that is netlogo. It shows how the algae is able to find food even without any meaningful sensory or neural organs, simply through its &quot;programming&quot;. The ability of the agents to find the shortest path can be considered an emergent property of the weak type. However, difficult obstacles can be a problem for the turtles to solve. This is because the pathing algorithm that is used doesn't take into calculation the obstacles. They are crossed through random tries and often cause the collectors to get stuck for some time. Improving on this problem would require a sophisticated pathing algorithm such as A* or Djikstra, which would defeat the purpose of the simulation.<br /> =Code=<br /> [[File:algae-xvegm00.nlogo]]</div>

Xvegm00 http://www.simulace.info/index.php?title=Simulation_of_semi-intelligent_algae&diff=16918 2019-01-09T12:13:22Z

<p>Xvegm00: </p> <hr /> <div>= Introduction =<br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> =Problem definition=<br /> The algae works in two modes: food search and optimisation. The food search part involves the algae spreading in a outward pattern, until it finds a foodsource. When it succeeds, it optimizes its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well.<br /> <br /> === Software ===<br /> <br /> Simulation is created using Netlogo 6.0.2.<br /> <br /> = Model =<br /> <br /> === Turtles ===<br /> The agents in our simulation are three by count.<br /> <br /> ==== Foods ====<br /> Foods are agents that stand for foodbits in a petri dish. They are not found at first, until an algae steps on them by chance and sets their patch color from blue to green. This means that the food is now found. There is an exception however, the middle food is found from the beginning, as an inital food source for the living algae.<br /> <br /> ==== Algae ====<br /> These agents are there to simulate algae spread. They dont simulate the whole area of the algae, that would be too expensive and inconvenient. Instead they stand as spreading tendrils of the algae. They color new patches yellow, which means the actual algae area. Their movement is random, although they only turn in random directions by 40°. This number seems to simulate the outward, radial pattern that the algae uses to find new food. It also creates nice veiny, fractal patterns.<br /> <br /> They are hatched on green spots (the food that has been discovered). They have a limited time to live, whenever they step on a new non-yellow tile, it is lowered. This makes sure that there are no tendrils separated from the main body, just like in reality, separated algae cells would die without a food source.<br /> <br /> ==== Collectors ====<br /> Collectors are agents represented by blue arrows. Their task is to optimize the algae. They do so by travelling from food to food and increasing the popularity variable of each yellow patch they step on.<br /> <br /> == Controls ==<br /> === Setup ===<br /> The setup button prepares the simulation environment. First the foods are distributed at preset coordinates. They are represented by static agents with their patch initially colored blue. Further foods can also be placed manually. Secondly the algae agents are hatched from the initial green food node and start their spread. Thirdly collectors are created at green spots as well.<br /> <br /> === Go ===<br /> <br /> to go<br /> make-collectors n-collectors<br /> make-algae physarums<br /> make-step<br /> move-collectors<br /> <br /> decay-popularity<br /> <br /> draw-turtles<br /> tick<br /> <br /> end<br /> <br /> <br /> =Results=<br /> <br /> =Conclusion=<br /> <br /> =Code=</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16917 2019-01-09T10:25:26Z

<p>Xvegm00: </p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;complexity&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0-19-515070-8 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. &lt;ref name=&quot;complexity2&quot;&gt;FROM COMPLEXITY TO LIFE &lt;i&gt;Niels Henrik Gregersen &lt;/i&gt;, Oxford University Press 2004, 256 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> There are two kinds of emergence, strong and weak.&lt;ref name=&quot;masfoundations&quot;&gt;FROM COMPLEXITY TO LIFE &lt;i&gt;Niels Henrik Gregersen &lt;/i&gt;, Oxford University Press 2004, 180 p. ISBN 0-19-515070-8 [cit. 2019-01-8]&lt;/ref&gt; Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”.&lt;ref name=&quot;agents&quot;&gt;Socially intelligent agents: creating relationships &lt;i&gt;Cathérine Tessier, Laurent Chaudron, Heinz-Jürgen Müller &lt;/i&gt;, Springer 2002, 298 p. 9780306469855 [cit. 2019-01-8]&lt;/ref&gt; Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities.&lt;ref name=&quot;systems2&quot;&gt;Multiagent Robotic Systems &lt;i&gt;Jiming Liu, Jianbing Wu &lt;/i&gt;, CRC Press 2002, 310 p. 9781420038835 [cit. 2019-01-8]&lt;/ref&gt; This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.&lt;ref name=&quot;systems&quot;&gt;Multiagent Robotic Systems &lt;i&gt;Jiming Liu, Jianbing Wu &lt;/i&gt;, CRC Press 2002, 156 p. 9781420038835 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> [[File:Algae1-xvegm00.jpg]]<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> ;Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> What the collector does so far is tread randomly between foods. So we need to make it visit all the foods consecutively and with the shortest path.<br /> <br /> We give collectors own list foodList that holds the unvisited foods, we give them own list goalList to hold foods already visited and finally listx as a temporary variable, as we need a list that foods can access<br /> <br /> <br /> to move-collectors<br /> ask collectors [<br /> let listx []<br /> if length goalList = count foods[<br /> set goalList []<br /> ]<br /> ifelse any? foods-on patch-here [<br /> set listx foodList<br /> if length listx = 0[<br /> ask foods [set listx fput self listx]<br /> ]<br /> ;we put visited foods to goalList<br /> if member? goal listx [<br /> set goalList fput goal goalList<br /> ;sorting foods by distance causes the list to fill with all the foods<br /> set listx sort-on [distance myself] foods<br /> ]<br /> let list3 []<br /> ;so we need to deduct the already visited foods from the sorted list<br /> foreach listx [<br /> [x] -&gt;<br /> if not member? x goalList [<br /> set list3 lput x list3<br /> ]<br /> ]<br /> set listx list3<br /> if length listx = 0 [<br /> let x min-one-of foods [distance myself]<br /> set listx fput x listx<br /> ]<br /> ;now we simply take the first goal on the sorted list<br /> set goal first listx<br /> walk-towards-goal<br /> set foodList listx<br /> ] [<br /> walk-towards-goal<br /> ]<br /> ]<br /> end<br /> <br /> This will cause the algae to choose the shortest paths properly.<br /> [[File:Algae2-xvegm00.jpg]]<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16916 2019-01-09T10:24:23Z

<p>Xvegm00: </p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;complexity&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0-19-515070-8 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. &lt;ref name=&quot;complexity2&quot;&gt;FROM COMPLEXITY TO LIFE &lt;i&gt;Niels Henrik Gregersen &lt;/i&gt;, Oxford University Press 2004, 256 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> There are two kinds of emergence, strong and weak.&lt;ref name=&quot;masfoundations&quot;&gt;FROM COMPLEXITY TO LIFE &lt;i&gt;Niels Henrik Gregersen &lt;/i&gt;, Oxford University Press 2004, 180 p. ISBN 0-19-515070-8 [cit. 2019-01-8]&lt;/ref&gt; Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”.&lt;ref name=&quot;agents&quot;&gt;Socially intelligent agents: creating relationships &lt;i&gt;Cathérine Tessier, Laurent Chaudron, Heinz-Jürgen Müller &lt;/i&gt;, Springer 2002, 298 p. 9780306469855 [cit. 2019-01-8]&lt;/ref&gt; Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities.&lt;ref name=&quot;systems&quot;&gt;Multiagent Robotic Systems &lt;i&gt;Jiming Liu, Jianbing Wu &lt;/i&gt;, CRC Press 2002, 310 p. 9781420038835 [cit. 2019-01-8]&lt;/ref&gt; This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.&lt;ref name=&quot;systems&quot;&gt;Multiagent Robotic Systems &lt;i&gt;Jiming Liu, Jianbing Wu &lt;/i&gt;, CRC Press 2002, 156 p. 9781420038835 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> [[File:Algae1-xvegm00.jpg]]<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> ;Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> What the collector does so far is tread randomly between foods. So we need to make it visit all the foods consecutively and with the shortest path.<br /> <br /> We give collectors own list foodList that holds the unvisited foods, we give them own list goalList to hold foods already visited and finally listx as a temporary variable, as we need a list that foods can access<br /> <br /> <br /> to move-collectors<br /> ask collectors [<br /> let listx []<br /> if length goalList = count foods[<br /> set goalList []<br /> ]<br /> ifelse any? foods-on patch-here [<br /> set listx foodList<br /> if length listx = 0[<br /> ask foods [set listx fput self listx]<br /> ]<br /> ;we put visited foods to goalList<br /> if member? goal listx [<br /> set goalList fput goal goalList<br /> ;sorting foods by distance causes the list to fill with all the foods<br /> set listx sort-on [distance myself] foods<br /> ]<br /> let list3 []<br /> ;so we need to deduct the already visited foods from the sorted list<br /> foreach listx [<br /> [x] -&gt;<br /> if not member? x goalList [<br /> set list3 lput x list3<br /> ]<br /> ]<br /> set listx list3<br /> if length listx = 0 [<br /> let x min-one-of foods [distance myself]<br /> set listx fput x listx<br /> ]<br /> ;now we simply take the first goal on the sorted list<br /> set goal first listx<br /> walk-towards-goal<br /> set foodList listx<br /> ] [<br /> walk-towards-goal<br /> ]<br /> ]<br /> end<br /> <br /> This will cause the algae to choose the shortest paths properly.<br /> [[File:Algae2-xvegm00.jpg]]<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16915 2019-01-09T10:04:50Z

<p>Xvegm00: </p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;masfoundations&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. <br /> There are two kinds of emergence, strong and weak. Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”. Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities. This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> [[File:Algae1-xvegm00.jpg]]<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> ;Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> What the collector does so far is tread randomly between foods. So we need to make it visit all the foods consecutively and with the shortest path.<br /> <br /> We give collectors own list foodList that holds the unvisited foods, we give them own list goalList to hold foods already visited and finally listx as a temporary variable, as we need a list that foods can access<br /> <br /> <br /> to move-collectors<br /> ask collectors [<br /> let listx []<br /> if length goalList = count foods[<br /> set goalList []<br /> ]<br /> ifelse any? foods-on patch-here [<br /> set listx foodList<br /> if length listx = 0[<br /> ask foods [set listx fput self listx]<br /> ]<br /> ;we put visited foods to goalList<br /> if member? goal listx [<br /> set goalList fput goal goalList<br /> ;sorting foods by distance causes the list to fill with all the foods<br /> set listx sort-on [distance myself] foods<br /> ]<br /> let list3 []<br /> ;so we need to deduct the already visited foods from the sorted list<br /> foreach listx [<br /> [x] -&gt;<br /> if not member? x goalList [<br /> set list3 lput x list3<br /> ]<br /> ]<br /> set listx list3<br /> if length listx = 0 [<br /> let x min-one-of foods [distance myself]<br /> set listx fput x listx<br /> ]<br /> ;now we simply take the first goal on the sorted list<br /> set goal first listx<br /> walk-towards-goal<br /> set foodList listx<br /> ] [<br /> walk-towards-goal<br /> ]<br /> ]<br /> end<br /> <br /> This will cause the algae to choose the shortest paths properly.<br /> [[File:Algae2-xvegm00.jpg]]<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16914 2019-01-09T09:59:40Z

<p>Xvegm00: </p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;masfoundations&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. <br /> There are two kinds of emergence, strong and weak. Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”. Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities. This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> [[File:Example.jpg]]<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> ;Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> What the collector does so far is tread randomly between foods. So we need to make it visit all the foods consecutively and with the shortest path.<br /> <br /> We give collectors own list foodList that holds the unvisited foods, we give them own list goalList to hold foods already visited and finally listx as a temporary variable, as we need a list that foods can access<br /> <br /> <br /> to move-collectors<br /> ask collectors [<br /> let listx []<br /> if length goalList = count foods[<br /> set goalList []<br /> ]<br /> ifelse any? foods-on patch-here [<br /> set listx foodList<br /> if length listx = 0[<br /> ask foods [set listx fput self listx]<br /> ]<br /> ;we put visited foods to goalList<br /> if member? goal listx [<br /> set goalList fput goal goalList<br /> ;sorting foods by distance causes the list to fill with all the foods<br /> set listx sort-on [distance myself] foods<br /> ]<br /> let list3 []<br /> ;so we need to deduct the already visited foods from the sorted list<br /> foreach listx [<br /> [x] -&gt;<br /> if not member? x goalList [<br /> set list3 lput x list3<br /> ]<br /> ]<br /> set listx list3<br /> if length listx = 0 [<br /> let x min-one-of foods [distance myself]<br /> set listx fput x listx<br /> ]<br /> ;now we simply take the first goal on the sorted list<br /> set goal first listx<br /> walk-towards-goal<br /> set foodList listx<br /> ] [<br /> walk-towards-goal<br /> ]<br /> ]<br /> end<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16913 2019-01-09T09:58:37Z

<p>Xvegm00: </p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;masfoundations&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. <br /> There are two kinds of emergence, strong and weak. Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”. Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities. This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> ;Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> What the collector does so far is tread randomly between foods. So we need to make it visit all the foods consecutively and with the shortest path.<br /> <br /> We give collectors own list foodList that holds the unvisited foods, we give them own list goalList to hold foods already visited and finally listx as a temporary variable, as we need a list that foods can access<br /> <br /> <br /> to move-collectors<br /> ask collectors [<br /> let listx []<br /> if length goalList = count foods[<br /> set goalList []<br /> ]<br /> ifelse any? foods-on patch-here [<br /> set listx foodList<br /> if length listx = 0[<br /> ask foods [set listx fput self listx]<br /> ]<br /> ;we put visited foods to goalList<br /> if member? goal listx [<br /> set goalList fput goal goalList<br /> ;sorting foods by distance causes the list to fill with all the foods<br /> set listx sort-on [distance myself] foods<br /> ]<br /> let list3 []<br /> ;so we need to deduct the already visited foods from the sorted list<br /> foreach listx [<br /> [x] -&gt;<br /> if not member? x goalList [<br /> set list3 lput x list3<br /> ]<br /> ]<br /> set listx list3<br /> if length listx = 0 [<br /> let x min-one-of foods [distance myself]<br /> set listx fput x listx<br /> ]<br /> ;now we simply take the first goal on the sorted list<br /> set goal first listx<br /> walk-towards-goal<br /> set foodList listx<br /> ] [<br /> walk-towards-goal<br /> ]<br /> ]<br /> end<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16912 2019-01-09T09:58:08Z

<p>Xvegm00: /* Multiagent simulation in Netlogo */</p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;masfoundations&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. <br /> There are two kinds of emergence, strong and weak. Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”. Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities. This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> ;Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> What the collector does so far is tread randomly between foods. So we need to make it visit all the foods consecutively and with the shortest path.<br /> <br /> We give collectors own list foodList that holds the unvisited foods, we give them own list goalList to hold foods already visited and finally listx as a temporary variable, as we need a list that foods can access<br /> <br /> <br /> to move-collectors<br /> ask collectors [<br /> let listx []<br /> if length goalList = count foods[<br /> set goalList []<br /> ]<br /> ifelse any? foods-on patch-here [<br /> set listx foodList<br /> if length listx = 0[<br /> ask foods [set listx fput self listx]<br /> ]<br /> ;we put visited foods to goalList<br /> if member? goal listx [<br /> set goalList fput goal goalList<br /> ;sorting foods by distance causes the list to fill with all the foods<br /> set listx sort-on [distance myself] foods<br /> ]<br /> let list3 []<br /> ;so we need to deduct the already visited foods from the sorted list<br /> foreach listx [<br /> [x] -&gt;<br /> if not member? x goalList [<br /> set list3 lput x list3<br /> ]<br /> ]<br /> set listx list3<br /> if length listx = 0 [<br /> let x min-one-of foods [distance myself]<br /> set listx fput x listx<br /> ]<br /> ;now we simply take the first goal on the sorted list<br /> set goal first listx<br /> walk-towards-goal<br /> set foodList listx<br /> ] [<br /> walk-towards-goal<br /> ]<br /> ]<br /> end<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16909 2019-01-08T21:39:14Z

<p>Xvegm00: /* Introduction */</p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.&lt;ref name=&quot;masfoundations&quot;&gt;Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations &lt;i&gt;Yoav Shoham, Kevin Leyton-Brown&lt;/i&gt;, Cambridge University Press 2009, 473 p. ISBN 0521899435 [cit. 2019-01-8]&lt;/ref&gt;<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. <br /> There are two kinds of emergence, strong and weak. Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”. Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities. This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> <br /> Now we can move the collectors.<br /> <br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16906 2019-01-08T21:31:22Z

<p>Xvegm00: </p> <hr /> <div>==Introduction==<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.<br /> <br /> ==Emergence==<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. <br /> There are two kinds of emergence, strong and weak. Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> <br /> ==Autonomous agents==<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”. Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> <br /> Agents have several characteristics:<br /> *Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> *Reactivity – agents react to events, they are capable of perceiving time<br /> *Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> *Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> <br /> Reactive agents<br /> <br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> <br /> ==Multiagent system==<br /> <br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities. This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> *Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> *There is no centralized control of the system<br /> *Data is decentralized<br /> *Calculations are done asynchronously<br /> *Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> <br /> ==Real life applications==<br /> <br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.<br /> <br /> ==Multiagent simulation in Netlogo==<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> <br /> This video shows the movement, food search and path optimization of the physarum. [https://www.youtube.com/watch?v=mvBSkt6LhJE]<br /> <br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> <br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> <br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> <br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> <br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> <br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> <br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> <br /> Now we can move the collectors.<br /> <br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> <br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.<br /> <br /> Author: Bc. Martin Vegner [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:30, 8 January 2019 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Multi-agent_systems&diff=16905 2019-01-08T21:22:01Z

<p>Xvegm00: Created page with &quot;Multiagent systems Author: Bc. Martin Vegner Introduction Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossib...&quot;</p> <hr /> <div>Multiagent systems<br /> Author: Bc. Martin Vegner<br /> Introduction<br /> Many organisms in the world function without complex nervous systems, yet they perform feats that seem to be impossible without decisive thinking. For example, the algae Physarum Polycephalum (literally the „many-headed slime“) are a slime mold that inhabits shady, cool, moist areas, such as decaying leaves and logs. It consists of many single cell organisms that merge together in a symbiotic relationship. It has no brain, not even a single neuron and yet it shows signs of intelligence. It is capable of solving the travelling salesmen problem (TSP for short), as it connects bits of food with shortest paths of itself, to establish a network with efficient nutrient transport. It avoids obstacles or harmful substances, it remembers and even seems to be able to share information between its cells. While, there are many mysteries about Physarum yet to be solved, we already know, how it is capable of solving the travelling salesman problem. Each of Physarum cells is programmed in such a way, that they are progressively able to optimize its paths. One Physarum cell is unable to determine anything, yet many cells can perform wonders. This phenomenon is called emergence.<br /> Emergence<br /> Emergence is described as condition of an entity having properties that its parts do not have. This property emerges from the interaction between those parts. <br /> There are two kinds of emergence, strong and weak. Weak emergence describes properties that could be derived from the properties of the single parts. This emergence can be easily reproduced in computer simulation. However, strong emergence describes entirely new properties that cannot be connected with the properties of the original parts. An example of strong emergence property is the human consciousness. There is no apparent way our neurons could produce a sense of consciousness, yet we obviously have it. It is an emergent property of the complex system that is our brain.<br /> Autonomous agents<br /> An agent is a simulated or artificial unit that simulates the behavior of an existing or theoretical lifeform. It can also be defined as “a singular entity in an environment, where it can perform or interact”. Agents perform tasks based on conditions they were programmed with. They don’t need to be aware of their surroundings or even of themselves. Their function is of automatons, they react to inputs, which can be sensory information or just internal information such as time, with predetermined reactions.<br /> Agents have several characteristics:<br /> • Autonomy – agents are proactive, they have target oriented modules, they are capable of solving problems without outside help, they have the ability to provide or ask for information<br /> • Reactivity – agents react to events, they are capable of perceiving time<br /> • Intentionality – agents focus on reaching long term goals using planning, forecasting and decision-making<br /> • Social behavior – agents can communicate and cooperate with others, they keep information about others and they might form groups<br /> There are several kinds of agents, but for the purpose of this text, two is enough:<br /> Reactive agents<br /> Reactive agents are the simplest kind of agents. They can be described as an entity that performs its action exclusively based on input from the environment. Their rationality is a direct result of interaction with the outside world. They cannot plan ahead nor make decisions with preference. These agents are usually composed of modules, where each module is activated by a certain condition. The main feature of reactive agents is reactivity, meaning they are able to react to the environment with the purpose of reaching their set goal.<br /> Deliberative agents<br /> Sometimes called intentional, these agents can reach a quality of reasoning that is close to that of people. Deliberative agents hold a symbolical representation of the environment, meaning a database of facts about the world. With such information, they are capable of evaluating, calculating and planning ahead. They can make decision about the priority of their tasks; however, they are still predetermined. They have no freedom in making decisions, they cannot reflect on their past and learn from it. They are capable of having different mental states and attitudes.<br /> Multiagent system<br /> Creating a multiagent system or a simulation is a way of testing a hypothesis or developing autonomous robots to perform tasks. The system consists of many connected entities. This connection may origin from actual communication between agents and agents with environment; however, this connection may just stem from the fact, that they share the environment and interact with it. There may be signs of order in the system, just as there is in the world and nature. However, these are just consequences of the programming of entities and their interaction. The system is not globally controlled and the agents make decisions for themselves. Intelligent behavior is either caused by a sophisticated algorithm or emergent properties.<br /> There are a few characteristics of multiagent systems:<br /> • Each agent has incomplete data about the environment or lacks the ability to solve the task at hand on its own<br /> • There is no centralized control of the system<br /> • Data is decentralized<br /> • Calculations are done asynchronously<br /> • Single algorithms are predictable, multiagent systems can lead to unpredictable results due to emergence<br /> Why use agent systems<br /> Agent systems are useful in a situation where global control is becoming too complex or costly. Let’s look at vacuuming your apartment. Controlling the actions of the agent directly means vacuuming yourself. Even if you were to control the vacuum remotely, nothing changes. It still costs you time and doesn’t allow vacuuming several places at once. Recently, however, autonomous vacuums (also called roombas) are on the rise. These are basically reactive agents. You can deploy as many of them as you like and unless they get stuck or deplete their battery, they work autonomously.<br /> Virtual agent systems such as in a simulation are useful for simulating an imaginary environment in which the result is unknown. Such simulation allows repetition, change of environment variables and evolution of the agents.<br /> Real life applications<br /> Agent systems are already widespread in today’s civilization. They are used whenever centralized systems with predetermined behavior aren’t sufficient. There can be two (not mutually exclusive) reasons. For one, it might be impossible, during the analysis of the applicable environment, to foresee the possible behavior of the environment. The second case is emergency situations. Centralized systems are prone to collapse in unknown situations. However, in an agent system, it does not matter that much, if few agents stop working, as the rest can still react. Due to this ability, agent systems can be used to control airspace and land transport alike. This includes even trains and ships. Today’s focus in development is autonomous vehicles, which are of course agents.<br /> An example of publicly functioning virtual agents are Google’s bots (also sometimes called spiders or crawlers). These are purely software agents that search the whole web and catalogue new pages as well as update the old ones. The purpose of their work is to index the internet to allow for fast function of our favorite search engine.<br /> Multiagent simulation in Netlogo<br /> Netlogo is an agent-based programming language and integrated modeling environment. It allows for the creation of simulations in Lisp based language Logo. Its purpose is mainly education, although it has been severely used in academic papers as well.<br /> As an example let us find a way to simulate the algae Physarum Polycephalum in Netlogo. Physarum spreads in its environment looking for food. As it finds several sources of food it connects them with its cells in an effective manner (shortest paths between nodes, using existing paths instead of creating new ones, optimizing existing paths).<br /> https://www.youtube.com/watch?v=mvBSkt6LhJE<br /> Our starting point is an initial node in the middle of the simulation area. It is represented by an agent – food. Our algae agents are created on this patch. <br /> First we need to simulate the spread of the algae. The cells have no vision, they can probably sense light levels and chemical properties of the environment. This however we cannot use. For the purposes of our simulation, the algae spread randomly.<br /> To make-step<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> end<br /> Using random we can make the algae turn in a random direction by a set number of degrees. It doesn’t really matter what is the number of degrees, we just need randomness to the movement. However, from trying we find that 40° stimulates outward motion that mimics the radial spread of the algae and also causes a fractal pattern to the resulting algae area.<br /> Now we need to draw the actual algae. Physarum is composed by single cells merged together; however having hundreds of agents in the simulation could be expensive. It is better to draw the algae as patches with certain color. You might object; but the algae also optimize its paths, thus it removes itself from area! To allow optimization, we give the patches a variable popularity, which describes how often the patch gets visited. Patches which are seldom visited get erased.<br /> ;the algae sets patches yellow on each step<br /> To make-step<br /> let popularity-per-step 50<br /> set randy random 2<br /> ifelse randy = 1 [rt 40][rt -40]<br /> fd 1<br /> set pcolor yellow<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> end<br /> ;this function is called every tick to lower the popularity of all yellow patches<br /> to decay-popularity<br /> ask patches with [ not any? algae-here and pcolor = yellow] [<br /> set popularity popularity - (popularity-decay-rate / 10)<br /> if popularity &lt; 1 [ set pcolor brown + 4]<br /> ]<br /> end<br /> This gives us a nice fractal and wildly spreading algae.<br /> Now we need something to optimize the pathways. So far the algae spreads and decays randomly. In reality, we believe that the flow of nutrients through the algae is what tells it where to stay a where to withdraw. Therefore, we need to find a way to let the nutrients flow. Let’s create a new agent and call him collector. Collectors are going to travel between food agents, but only on the yellow patches. Setting direction to the nearest food agent is easy, but how do we make it choose paths with only yellow patches?<br /> Lets create a reporter class to tell our agent where to go.<br /> to-report best-way-to [ destination ]<br /> ; of all the visible route patches, select the ones<br /> ; that would take me closer to my destination<br /> set collector-vision-dist 30<br /> let visible-patches patches in-radius collector-vision-dist<br /> let visible-routes visible-patches with [ pcolor = 45]<br /> let routes-that-take-me-closer visible-routes with <br /> [ distance destination &lt; [ distance destination - 1 ] of myself ]<br /> <br /> ifelse any? x [<br /> ; from those route patches, choose the one that is the closest to me<br /> report min-one-of routes-that-take-me-closer [ distance self ]<br /> ] [<br /> ; if there are no nearby routes to my destination<br /> report destination<br /> ]<br /> end<br /> Now we can move the collectors.<br /> to move-collectors<br /> let popularity-per-step 1000<br /> ask collectors [ <br /> ;standing on food is the best time to set the next goal<br /> ifelse any? foods-on patch-here [ <br /> set goal one-of other foods<br /> face best-way-to goal<br /> jump 1<br /> ;each step we strongly increase the popularity of the patch<br /> ask patch-here [ <br /> set popularity popularity + popularity-per-step<br /> ]<br /> ]<br /> End<br /> With enough ticks passed, the algae optimizes nicely and connects all of the food nodes. But wait, that’s not correct, is it? Physarum is able to solve the shortest path problem, that means not only connecting the nodes, but also making connections only when they are actually shorter than existing ones. To remedy this problem, we need some serious adjustments.</div>

Xvegm00 http://www.simulace.info/index.php?title=Simulation_of_semi-intelligent_algae&diff=16904 2019-01-08T21:20:28Z

<p>Xvegm00: Created page with &quot;== Intro == This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two...&quot;</p> <hr /> <div>== Intro ==<br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> The algae works in two modes: food search and optimisation. The food search part involves the algae spreading in a outward pattern, until it finds a foodsource. When it succeeds, it optimizes its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well.<br /> <br /> === Software ===<br /> <br /> Simulation is created using Netlogo 6.0.2.</div>

Xvegm00 http://www.simulace.info/index.php?title=WS_2018/2019&diff=16903 2019-01-08T21:13:30Z

<p>Xvegm00: </p> <hr /> <div>Semestral papers from winter term 2018/2019. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2018/2019|assignment approved]]<br /> <br /> ==Simulations==<br /> <br /> --[[User:xvegm00|xvegm00]] [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 22:13, 8 January 2019 (CET) [[Simulation of semi-intelligent algae]]</div>

Xvegm00 http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16902 2019-01-08T21:09:24Z

<p>Xvegm00: /* Simulation proposal (Xvegm00) Simulation of semi-intelligent algae (Not approved yet) */</p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> == Simulation proposal ([[Xvegm00|Xvegm00]]) Simulation of semi-intelligent algae ==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> The algae should work in two modes: food search and optimisation. The food search part involves the algae spreading in a radial pattern, until it finds a foodsource. When it succeeds, it should optimise its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well. A test of function could be for the algae to find a path through a wall with three holes and to choose the proper hole (with shortest path).<br /> <br /> Initial plan<br /> There is an initial node, where the algae begins and from which it spreads.<br /> Food is randomly distributed across the area, defined be a patch of certain color.<br /> Algae spreads as agents, leaving behind patches as temporary algae. Temporary algae have a time to live, a timeout. When foodsource is found, nutrients travel as agents back to to initial node. When nutrients visit an algae patch, they reset its timeout. Thus, only the algae which has nutrients travelling on it can survive. With a properly applied randonmness, for the travel of nutrients as well as the agents, this should optimise the pathways.<br /> <br /> ''Assignment''<br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 13:23, 30 December 2018 (CET)<br /> <br /> : Generally, it could be done, but it is necessary to elaborate this assignment into greater detail. How you will measure the results? How you can say if it was successful or not? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:21, 31 December 2018 (CET)<br /> :: Asiggnment updated. Please revise. [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 14:33, 31 December 2018 (CET)<br /> ::: Ok, now I understand it better, how the simulation works, and it is fine. The only questionmark is still the results. What is the problem you study? What is the question you solve? How do you verify, your simulation solved the problem? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:30, 31 December 2018 (CET)<br /> :::: It solves the travelling salesmen problem, which is algoritmically NP complete problem. However, it is solved through agents, which incorporates emergent intelligence, through orderly behaviour of agents. It explains how a group of algae, without any neurons or thinking organs, solves difficult problems. It shows, that there is a simple and low requirement solution to the problem. It offers a different solution to calculating the path, instead a simulation can be used to plan roads between cities for example. It can be verified, as i suggested in the assignment, by making it find the shortest path arround obstacles. It even adapts to changes in the topology.[[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 23:26, 1 January 2019 (CET)<br /> ::::: '''Accepted''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 11:53, 8 January 2019 (CET)<br /> <br /> == Simulation proposal ([[xkorj58|xkorj58]]) Find the thief (Not approved yet)==<br /> <br /> This simulation should be created as a space search. There shall be two types of agents - first ones, police officers looking for a thief. Second a thief (moveable or imoveable) which is about to be found. Room itself could have obstacles. Police officers will have a vision, can have memory, could be various number of them and level of shared information about searched fields.<br /> <br /> ''Description''<br /> <br /> Environment will have certain number of walls through which agent (police officer) can't see. In this environment will be thief to find (moving or stable). It will be a closed environment (walled from all sides), for the agent information about the environment will be inaccessible, he will not know, where the walls are and how many of them there is. It will be deterministic - every step of one of agents will have just one effect - on agent itself. This leads to a static environment - agents will be the only moving things in it. As the environment is closed it will have certain number of possible actions. Thus it will be discrete and dimensional.<br /> <br /> Agents will at first randomly run through the space. It will have a vision (few patches in front of him). It will also save information about what he saw, therefore he will know where should he go to explore unseen locations. From this comes his intelligence. In situation with more agents, they will share this information about space and create for themselves a map of the environment (already searched patches).<br /> <br /> ''Assignment''<br /> <br /> Title: Find the thief<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Jiří Korčák<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 16:03, 17 December 2018 (CET)<br /> <br /> : Sorry, but I don't understand what is this simulation good for. What meaningful problem do you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:44, 31 December 2018 (CET)<br /> ::The problem should be about how memory, number of police officers and shared informations influence searching for a thief. It could have applications for AI in computer games (thats where you meet agents like this ones) or even in real life - most important thing when looking for something is: ... (when there is 20 of us looking for something, memory and shared information is not that important, etc.). [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 10:36, 31 December 2018 (CET)<br /> ::: The assignment is too brief. For the real situation, you definitely need stated where do you find relevant data to be able setting the parameters of the simulation. If you want to develop an intelligent agent for using in e.g. computer games, I am fine with that, but you need to define the environment, you need to define, how the agent should exactly behave, how his intelligence works, etc.<br /> ::: Please, choose one of the approaches (I would recommend the second one), and describe the problem really in detail. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:35, 31 December 2018 (CET)<br /> :::: I think, I'll pick the second one. Up in describtion I tried to define the environment and the agents intelligence. I'm not sure if it's complex enough. What do you recommend in this situation and simulation to upgrade? Escpecially about the agents (officers and thiefs). Thanks for your answer. [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 02:48, 2 January 2019 (CET)<br /> ::::: Do I understand well, that this way you explore an unknown area? It could be a good task, this is something e.g. robots need to do. Why do you need the thief? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 11:57, 8 January 2019 (CET)<br /> ::::::I think I need it to measure time of the search. Like a point to be reached. I suppose it's better than just exploring the room as a whole. You think it would be better to just measure time needed to explore every patch in a room? [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 19:58, 8 January 2019 (CET)<br /> <br /> == Simulation proposal (dolj04) ==<br /> <br /> Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)&lt;br/&gt;<br /> - the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)&lt;br/&gt;<br /> - the average size is not changing that much&lt;br/&gt;<br /> - (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)&lt;br/&gt;<br /> - backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)&lt;br/&gt;<br /> - successfully processed batches older than 14 days are deleted every day&lt;br/&gt;<br /> - for precaution lets say around 5% of batches wont be processed correctly&lt;br/&gt;<br /> - those will stay on the server and will be processed every month by admins &lt;br/&gt;<br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br /> <br /> :::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br /> <br /> ::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br /> <br /> == Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''likelihood of infection with flu'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population&lt;br/&gt;<br /> <br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br /> <br /> :::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br /> :::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br /> :::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''Social and economical effects of reunification of North &amp; South Korea''' &lt;br/&gt; &lt;br/&gt;<br /> '''Definition of the problem:''' &lt;br/&gt;<br /> Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: &lt;br/&gt;<br /> - North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. &lt;br/&gt;<br /> - South Koreans will invest in North Korea area and create jobs and factories there. &lt;br/&gt; &lt;br/&gt;<br /> ''I would like to simulate:'' &lt;br/&gt;<br /> - What will be the population ratio of the two areas per square meter. &lt;br/&gt;<br /> - How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. &lt;br/&gt;<br /> - How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. &lt;br/&gt;<br /> - What will be the housing situation after the reunification. &lt;br/&gt;<br /> - How will the GDP of the new country possibly develop. &lt;br/&gt;<br /> - Possibly how much money will be needed from international help. &lt;br/&gt; &lt;br/&gt;<br /> '''Simulation environment:''' NetLogo<br /> <br /> ----<br /> : I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br /> ----<br /> :: Thank you for the feedback - I tried to break down and simplify the simulation:<br /> <br /> :: '''Agents:'''<br /> <br /> :: - North Koreans (0-14 years)<br /> :: - North Koreans (15-24 years)<br /> :: - North Koreans (25-54 years)<br /> :: - North Koreans (55-64 years)<br /> :: - North Koreans (65 years and over)<br /> <br /> :: - South Koreans (0-14 years)<br /> :: - South Koreans (15-24 years)<br /> :: - South Koreans (25-54 years)<br /> :: - South Koreans (55-64 years)<br /> :: - South Koreans (65 years and over)<br /> <br /> :: - South Korea listed companies<br /> :: - Foregin investors<br /> <br /> :: '''Parameters'''<br /> :: - North Korea GDP<br /> :: - North Korea GDP growth<br /> <br /> :: - South Korea GDP<br /> :: - South Korea GDP growth<br /> <br /> :: - North Korea area [sq. meters]<br /> :: - South Korea area [sq. meters] <br /> <br /> :: - North Korea unemployment rate<br /> :: - South Korea unemployment rate<br /> :: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br /> <br /> :: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br /> :: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br /> <br /> :: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br /> <br /> :: '''Targets of the simulation:'''<br /> <br /> :: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br /> :: - Determine unemployment in the reunited country<br /> :: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br /> :: - Determine new GDP based on above data (foreign investors being random)<br /> <br /> :: '''Data sources:'''<br /> <br /> :: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br /> <br /> :: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br /> <br /> :: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br /> <br /> :: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br /> <br /> ::: The relevance of comparison with Germany is questionable, but, ok. Let's say there perhaps isn't any better comparison. Nevertheless, I still don't understand, how the simulation should work. How the agents will act? How it will be measured? How you will be able to calculate all the figures? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:28, 21 December 2018 (CET)<br /> <br /> :::: OK, I thought about how I would realize the simulation in NetLogo and I see you were right about it being too complicated. So I tried to simplify my model even more to be doable:<br /> :::: - The patches will be divided proportionally by area of the two countries, they will also be characterized by a certain portion of the initial GDP as their wealth.<br /> :::: - The turtles representing Koreans will have initial wealth (also based on the initial GDP) and they will be migrating towards one of the two states based on their inclination to migrate to the other country. This inclination will be calculated randomly based on the East-West Germany migration data and their wealth.<br /> :::: - On each tick, the simulation will determine:<br /> ::::: - if the turtle (Korean) has a job (landed one, got fired, is working, or didn't get hired) based semi-randomly on the wealth of the patch the turtle is on.<br /> ::::: - The wealth of the patch and turtle -&gt; if the turtle is employed, it gains wealth and also generates a little wealth for the patch; If unemployed, it drains the wealth of the patch a little and its wealth decreases a little.<br /> ::::: - Based on the suicide rates, employment and wealth, it will also semi-randomly decide if the person is likely to kill themselves.<br /> :::: That way we can at least simulate and see the wealth distribution, suicide rates and population distribution in a very simplified model... Does that makes sense or am I still in over my head? :) [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 19:20, 21 December 2018 (CET)<br /> <br /> ::::: To be honest, I am a bit skeptical about the results. As the assignment is based on vague assumptions, I think the results will be very general. On the other hand, I appreciate the level of preparation, which is above average. Hence, it is exceptionally '''approved''' and I hope you invest the same level of effort into the simulation itself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:52, 31 December 2018 (CET)<br /> :::::: Thank you! [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 12:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br /> <br /> Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).&lt;br/&gt;<br /> - Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).&lt;br/&gt;<br /> - Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.&lt;br/&gt;<br /> - Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.&lt;br/&gt;<br /> <br /> '''Simulation environment:''' Netlogo<br /> '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:29, 21 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Maze runner vs fire'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There will be n apartment with rooms and obstacles (tables, bed, bath...), in a random room will be a people (user selects how many), who will try to find the way out, in some other random place will be fire spreading rapidly. Agents will not be able to move through walls and obstacles. The purpose of this simulation is to simulate an escape plan for an apartment. <br /> <br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : What is the purpose of the simulation? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:30, 21 December 2018 (CET)<br /> :: to find out whether a person who is lost in a building will survive. Imagine yourself in a bulding you have never been before and a fire appears. Wouldn’t it be interesting to know If you would find your way out? [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 22:51, 21 December 2018 (CET)<br /> ::: It is just a modification of our Building escape example. Ok, it could be doable, but there must be a clear and substantial added value. Your assignment proposal must be much much more deeply elaborated. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:56, 31 December 2018 (CET)<br /> :::: Ok, edited. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 01:53, 3 January 2019 (CET)<br /> ::::: This is nothing extra original, but '''approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 11:59, 8 January 2019 (CET)<br /> ----<br /> <br /> ==Simulation proposal [[User:qnesa01|qnesa01]] ==<br /> <br /> Topic/goal: '''Traffic simulation Argentinska street'''<br /> <br /> '''Situation:'''&lt;br/&gt;<br /> - Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br /> <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br /> <br /> <br /> '''Simulation environment''': Simprocess; SUMO (for traffic representation) <br /> <br /> '''Brief process of simulation :'''&lt;br/&gt;<br /> 1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br /> 2) Real situation simulation <br /> 3) Based on simulation of real situation will be checked the efficiency of lights changes <br /> 4) The hypothetical model of adding one more line will be created based on simulation of real situation <br /> 5) Summary<br /> <br /> : Ok, SUMO could be used for this. I just don't understand what you will simulate in SUMO, and what you will simulate in Simprocess, and why? The combination of two tools is uneasy, so there should be a good reason. However sounds interesting. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:41, 21 December 2018 (CET)<br /> : The Simprocess will be used for problem solution and SUMO for traffic representation of ready solution. Because SUMO does not have strong performance in searching solution, but very good for clear representation of ready result. Combination of two tools will be useful for this case. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 21:47, 27 December 2018 (CET)<br /> :: Sorry, I still don't understand. Please, could you describe, how the simulation will look like in Simprocess and in SUMO? What will be the data you pass from SUMO to Simprocess (or vice versa)? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:36, 31 December 2018 (CET)<br /> <br /> ::: As I have mentioned above, firstly I am going to simulate initial state (as it is now). This will be done in Simprocess and SUMO. In Simprocess will be used variables: time of lights change, distribution of cars number arriving, staying and leaving. Based on same variables, simulation in SUMO will be created to get map based view. Secondly, I would like to work with variables to answer the proposed questions. All changes will be done first in Simprocess to get optimal solution. And then based again on the result values of variables from Simprocess the map view in SUMO will be made. So, SUMO will be used for representation purpose, to show Simprocess solution on the map. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 11:27, 3 January 2019 (CET) <br /> :::: I strongly recommend to stick to one environment. Nevertheless, I leave it up to you. '''Approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 12:04, 8 January 2019 (CET)<br /> ----<br /> <br /> == Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) (Reworked, Not Approved) ==<br /> <br /> <br /> <br /> Topic/goal: '''Slime mold simulation (Reworked, Not Approved)'''&lt;br/&gt;<br /> <br /> '''Explanation:'''&lt;br/&gt;<br /> Slime mold (or more accurately physarum polycephalum) is a slime mold that inhabits shady, cool, moist areas,<br /> such as decaying leaves and logs. Like slime molds in general, it is sensitive to light; in particular, <br /> light can repel the slime mold and be a factor in triggering spore growth.<br /> <br /> The interesting factor of this mold for my purpose of simulation is the smart-like path finding. This was<br /> demonstrated by Harvard Magazine in a topic of solving a simple 2D maze, with finding the shortest path.<br /> Also in another demonstration was proven, when the researchers place food at cities on the map, the fungus<br /> collaborates, spreading out to map many possible configurations and then dying away to highlight the shortest<br /> routes between cities and the most efficient overall system map. For more see: link (http://harvardmagazine.com/2010/05/networked-web-extra)<br /> <br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> The ability to find the most effective route from one source of food to another is the key ability I'll be<br /> using for my simulation. The behavior and the end result can be easily checked against the realistic data<br /> provided from the recorded studies. The agents will be the mold unit itself, which is directly depended on<br /> the neighbor agents and the environment. Parameters can be changed to create different environment and then<br /> can be compared what would be the minimum strength/endurance + speed to according numbers of food sources.<br /> <br /> '''Key parameters:'''<br /> <br /> -number of &quot;food&quot; in the area (generated randomly)&lt;br/&gt;<br /> -number of origin points of the slime mold (generated randomly)&lt;br/&gt;<br /> -speed of spreading in which the mold is finding the food&lt;br/&gt;<br /> -strength/endurance of the mold - the ability to survive without food (effectively it is the &quot;searching range&quot; of the mold)&lt;br/&gt;<br /> <br /> '''Simulation environment''': Netlogo&lt;br/&gt;<br /> <br /> : Reworked into more detailed version as requested. [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 15:21, 7 January 2019 (CET)<br /> '''Approved'''. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 12:05, 8 January 2019 (CET)<br /> ----<br /> <br /> == Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Medieval Battle Simulation'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.&lt;br/&gt;<br /> Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).&lt;br/&gt;<br /> Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.&lt;br/&gt;<br /> When they meet in battle, they fight to the last man who wins the dispute for his king.&lt;/br&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> It simulates medieval combat on battlefield between two sides.&lt;br/&gt;<br /> The user can select which type of units and how many will each kingdom have.&lt;br/&gt;<br /> Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br /> <br /> '''Simulation environment:''' NetLogo<br /> <br /> : I would narrow this down to testing battle strategies. Forget kingdoms. You can perform a research on some historical strategies, their pros and cons, choose some, and try to simulate them, and compare. If it sounds meaningful, please, redefine the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:48, 21 December 2018 (CET)<br /> <br /> :: The part about kingdoms was more of a story telling. The objects in NetLogo would just be the units (of different types). I envisioned them to spawn at random on their given side of the plane, but if it's necessary I could include spawning in historical formations. Would that be ok? [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 11:00, 22 December 2018 (CET)<br /> <br /> ::: The assignment is too brief to check. Please, define it in detail. Theoretically, the assignment should be the only source for you to solve it. It means that in this phase you should have thought out most of the details. Which formations you will simulate, how exactly should it look like, what will be the results, how you will evaluate it... [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:43, 31 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> At the moment we have a venue in a small town under construction which we would like to turn into a café.&lt;br/&gt; <br /> The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. &lt;br/&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.&lt;br/&gt; <br /> I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.&lt;br/&gt;<br /> &lt;br/&gt;<br /> The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br /> <br /> '''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br /> <br /> : I like the idea, just hesitate about the tool. As far as I understand, a substantial part of the whole thing is about the spatial arrangement of the café. Then Simprocess is not the first choice. Why do you want to use it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:56, 21 December 2018 (CET)<br /> <br /> ::I liked that Simprocess allowed me to work with resources. And I see the tables and chairs as a resource, as well as the working power. If the flow of people coming in is well set up, I thought, by adjusting the resources, it should be possible to find out the optional setting. Do you propose any other tool to do that? I will be greatful for other ideas to improve the solution as it is a real problem we would like to solve. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 10:21, 27 December 2018 (CET)<br /> <br /> ::: If you need to deal with spatial factors, NetLogo is definitely a better choice. If you omit the spatial characteristics, the simulation become trivial, what would not be enough for this paper. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:06, 31 December 2018 (CET)<br /> <br /> :::: Ok, I can do the simulation in NetLogo and mind the spatial characteristics. I just thought the original idea was complex enought as it would put together several rather simple simulations and tasks - like finding out the right amount of visitors based on data from people that run cafés in different places (recalculating the number of visitors based on the wealthiness of the region, based on the capacity of the venue, the number of citizens and so on). <br /> ::::I believe I can go with netLogo for simulating the venue as well, although it is not completely clear now for me, how do I find out the right ammount of seats for example?<br /> ::::I can imagine a random movement of agents coming in taking a first not-taken seat. It can also be like the agent has a capacity (it can be group of 4 or just a pair or a single person visitor) and it could take the table/seat only in case there is enough space for the whole group. There could also be a quality parameters - like grading system for the seats - how far is it from the bar, is it near the toilet and so on. So yes, that might show that some places will be more popular then others. Also the agents might have a limit under which they dont go, like if they dont find a seat with quality high enough they will leave. Now when I am writing this down I think it might work although I still have some questions. Is this something you had in mind as well or am I on completely different track? Thanks. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 11:26, 31 December 2018 (CET)<br /> <br /> ::::: All right, now I understand it better. For the purpose you mentioned, Simprocess could be fine. The only problem is that the simulation itself is rather simple. Let's stick to it, nevertheless, I will require to make it really in detail including obtaining real data and simulating all relevant aspects. '''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br /> <br /> '''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.<br /> <br /> :: I just wonder, where is the randomnes, as you want to just derive the configuration from the already operating competition. If it should be a simulation, you would have to have real daily data (for reasonable long period, a year minimum) for the demand for the flights (there and back) and also for the individual classes. Only then you are able to derive probability distributions for the simulation that would make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:31, 21 December 2018 (CET)<br /> <br /> <br /> ::: Ok, these are not available, so I will try a completely different idea:<br /> <br /> <br /> Topic/goal: '''Archiving maximum efficiency in ticket management of a small company (real-life based data)'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> A small company has support personnel, testers and developers in one office. They together solve tickets created by customers. These tickets sometimes get stuck in ticketing system, because the numbers of people working there are not perfect. Campany uses Kayako and Jira to manage tickets - from these systems, there will be a lot of real-life data to base the simulation on. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> The simulation aims to provide the ideal amount of support people, testers/analysts and developers to solve tickets as quick as possible, while there are constraints, such as company budget and lowered effectivity of too many people working on the same ticket. <br /> <br /> '''Simulation environment:''' I would like to use Simprocess to model and optimize the workflow.<br /> : All right, '''approved''', but bear in mind that a detailed simulation based on real data is expected.<br /> <br /> == Simulation proposal (xpipj04) ==<br /> <br /> Topic/goal: You are what you eat<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> Most people nowadays want to hit the gym or eat healthy to get fit and at the same time take a long high quality sleep and work effectively. All the success in life and your energy comes from the food (nutrition values) you consume a day and the regime you have. The new year is knocking the door and you want to make sure that you will plan your food and regime effectively so your life is in balance. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Based on the input you choose the simulation should be able to give you the expected levels of achieved energy, work efficiency and especially if you are about to get fit or you will drop down to be a couch potato. <br /> <br /> '''Input parameters'''&lt;br/&gt;<br /> - Food consumed - listed types of food with calculated nutrition values (protein, fat, sugar, energy) &lt;br/&gt;<br /> - Time spent at gym (kardio, workout, culturist) &lt;br/&gt;<br /> - Time spent at work - type of work (manual, manager) &lt;br/&gt;<br /> - Time spent sleeping &lt;br/&gt;<br /> <br /> '''Output'''&lt;br/&gt;<br /> - Your BMI &lt;br/&gt;<br /> - Your fitness level &lt;br/&gt;<br /> - Your work focus &lt;br/&gt;<br /> - Your energy level &lt;br/&gt;<br /> <br /> '''Where I will get the data? '''&lt;br/&gt;<br /> - My roommate is a nutrition specialist and member of huge fitness project so his advice will be the source of my dependencies between values as well as values for different types of nutrition. &lt;br/&gt;<br /> - Nutrition plans.&lt;br/&gt; <br /> <br /> '''How will I get the output?'''&lt;br/&gt;<br /> I would like to use NetLogo to be able to see both values (numbers) and also monthly progress on the display (maybe a characted running between job, eating and gym. Showing HP bar? :D).<br /> If this kind of simulation is not suitable for NetLogo then Vensim? I have no idea how would I transfer the problem into equitations which are required.<br /> <br /> :: You got it right - it is Vensim topic without any doubt. Without mathematical formulation you can not do any simulation, so you have to deal with mathematical model formulation anyway.So, if Vensim is ok with you, than we can approve it. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:20, 23 December 2018 (CET)<br /> :::: Then I will go with Vensim and try to put together something meaningful! [[User:Jan.pippal|Jan Pippal]]<br /> :::::: OK. Then '''approved'''. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 12:29, 24 December 2018 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16881 2019-01-07T10:02:55Z

<p>Xvegm00: /* Simulation proposal (Xvegm00) Simulation of semi-intelligent algae (Not approved yet) */</p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> == Simulation proposal ([[Xvegm00|Xvegm00]]) Simulation of semi-intelligent algae (Not approved yet)==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> The algae should work in two modes: food search and optimisation. The food search part involves the algae spreading in a radial pattern, until it finds a foodsource. When it succeeds, it should optimise its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well. A test of function could be for the algae to find a path through a wall with three holes and to choose the proper hole (with shortest path).<br /> <br /> Initial plan<br /> There is an initial node, where the algae begins and from which it spreads.<br /> Food is randomly distributed across the area, defined be a patch of certain color.<br /> Algae spreads as agents, leaving behind patches as temporary algae. Temporary algae have a time to live, a timeout. When foodsource is found, nutrients travel as agents back to to initial node. When nutrients visit an algae patch, they reset its timeout. Thus, only the algae which has nutrients travelling on it can survive. With a properly applied randonmness, for the travel of nutrients as well as the agents, this should optimise the pathways.<br /> <br /> ''Assignment''<br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 13:23, 30 December 2018 (CET)<br /> <br /> : Generally, it could be done, but it is necessary to elaborate this assignment into greater detail. How you will measure the results? How you can say if it was successful or not? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:21, 31 December 2018 (CET)<br /> :: Asiggnment updated. Please revise. [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 14:33, 31 December 2018 (CET)<br /> ::: Ok, now I understand it better, how the simulation works, and it is fine. The only questionmark is still the results. What is the problem you study? What is the question you solve? How do you verify, your simulation solved the problem? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:30, 31 December 2018 (CET)<br /> :::: It solves the travelling salesmen problem, which is algoritmically NP complete problem. However, it is solved through agents, which incorporates emergent intelligence, through orderly behaviour of agents. It explains how a group of algae, without any neurons or thinking organs, solves difficult problems. It shows, that there is a simple and low requirement solution to the problem. It offers a different solution to calculating the path, instead a simulation can be used to plan roads between cities for example. It can be verified, as i suggested in the assignment, by making it find the shortest path arround obstacles. It even adapts to changes in the topology.[[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 23:26, 1 January 2019 (CET)<br /> <br /> == Simulation proposal ([[xkorj58|xkorj58]]) Find the thief (Not approved yet)==<br /> <br /> This simulation should be created as a space search. There shall be two types of agents - first ones, police officers looking for a thief. Second a thief (moveable or imoveable) which is about to be found. Room itself could have obstacles. Police officers will have a vision, can have memory, could be various number of them and level of shared information about searched fields.<br /> <br /> ''Description''<br /> <br /> Environment will have certain number of walls through which agent (police officer) can't see. In this environment will be thief to find (moving or stable). It will be a closed environment (walled from all sides), for the agent information about the environment will be inaccessible, he will not know, where the walls are and how many of them there is. It will be deterministic - every step of one of agents will have just one effect - on agent itself. This leads to a static environment - agents will be the only moving things in it. As the environment is closed it will have certain number of possible actions. Thus it will be discrete and dimensional.<br /> <br /> Agents will at first randomly run through the space. It will have a vision (few patches in front of him). It will also save information about what he saw, therefore he will know where should he go to explore unseen locations. From this comes his intelligence. In situation with more agents, they will share this information about space and create for themselves a map of the environment (already searched patches).<br /> <br /> ''Assignment''<br /> <br /> Title: Find the thief<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Jiří Korčák<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 16:03, 17 December 2018 (CET)<br /> <br /> : Sorry, but I don't understand what is this simulation good for. What meaningful problem do you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:44, 31 December 2018 (CET)<br /> ::The problem should be about how memory, number of police officers and shared informations influence searching for a thief. It could have applications for AI in computer games (thats where you meet agents like this ones) or even in real life - most important thing when looking for something is: ... (when there is 20 of us looking for something, memory and shared information is not that important, etc.). [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 10:36, 31 December 2018 (CET)<br /> ::: The assignment is too brief. For the real situation, you definitely need stated where do you find relevant data to be able setting the parameters of the simulation. If you want to develop an intelligent agent for using in e.g. computer games, I am fine with that, but you need to define the environment, you need to define, how the agent should exactly behave, how his intelligence works, etc.<br /> ::: Please, choose one of the approaches (I would recommend the second one), and describe the problem really in detail. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:35, 31 December 2018 (CET)<br /> :::: I think, I'll pick the second one. Up in describtion I tried to define the environment and the agents intelligence. I'm not sure if it's complex enough. What do you recommend in this situation and simulation to upgrade? Escpecially about the agents (officers and thiefs). Thanks for your answer. [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 02:48, 2 January 2019 (CET)<br /> <br /> == Simulation proposal (dolj04) ==<br /> <br /> Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)&lt;br/&gt;<br /> - the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)&lt;br/&gt;<br /> - the average size is not changing that much&lt;br/&gt;<br /> - (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)&lt;br/&gt;<br /> - backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)&lt;br/&gt;<br /> - successfully processed batches older than 14 days are deleted every day&lt;br/&gt;<br /> - for precaution lets say around 5% of batches wont be processed correctly&lt;br/&gt;<br /> - those will stay on the server and will be processed every month by admins &lt;br/&gt;<br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br /> <br /> :::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br /> <br /> ::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br /> <br /> == Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''likelihood of infection with flu'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population&lt;br/&gt;<br /> <br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br /> <br /> :::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br /> :::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br /> :::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''Social and economical effects of reunification of North &amp; South Korea''' &lt;br/&gt; &lt;br/&gt;<br /> '''Definition of the problem:''' &lt;br/&gt;<br /> Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: &lt;br/&gt;<br /> - North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. &lt;br/&gt;<br /> - South Koreans will invest in North Korea area and create jobs and factories there. &lt;br/&gt; &lt;br/&gt;<br /> ''I would like to simulate:'' &lt;br/&gt;<br /> - What will be the population ratio of the two areas per square meter. &lt;br/&gt;<br /> - How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. &lt;br/&gt;<br /> - How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. &lt;br/&gt;<br /> - What will be the housing situation after the reunification. &lt;br/&gt;<br /> - How will the GDP of the new country possibly develop. &lt;br/&gt;<br /> - Possibly how much money will be needed from international help. &lt;br/&gt; &lt;br/&gt;<br /> '''Simulation environment:''' NetLogo<br /> <br /> ----<br /> : I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br /> ----<br /> :: Thank you for the feedback - I tried to break down and simplify the simulation:<br /> <br /> :: '''Agents:'''<br /> <br /> :: - North Koreans (0-14 years)<br /> :: - North Koreans (15-24 years)<br /> :: - North Koreans (25-54 years)<br /> :: - North Koreans (55-64 years)<br /> :: - North Koreans (65 years and over)<br /> <br /> :: - South Koreans (0-14 years)<br /> :: - South Koreans (15-24 years)<br /> :: - South Koreans (25-54 years)<br /> :: - South Koreans (55-64 years)<br /> :: - South Koreans (65 years and over)<br /> <br /> :: - South Korea listed companies<br /> :: - Foregin investors<br /> <br /> :: '''Parameters'''<br /> :: - North Korea GDP<br /> :: - North Korea GDP growth<br /> <br /> :: - South Korea GDP<br /> :: - South Korea GDP growth<br /> <br /> :: - North Korea area [sq. meters]<br /> :: - South Korea area [sq. meters] <br /> <br /> :: - North Korea unemployment rate<br /> :: - South Korea unemployment rate<br /> :: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br /> <br /> :: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br /> :: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br /> <br /> :: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br /> <br /> :: '''Targets of the simulation:'''<br /> <br /> :: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br /> :: - Determine unemployment in the reunited country<br /> :: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br /> :: - Determine new GDP based on above data (foreign investors being random)<br /> <br /> :: '''Data sources:'''<br /> <br /> :: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br /> <br /> :: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br /> <br /> :: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br /> <br /> :: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br /> <br /> ::: The relevance of comparison with Germany is questionable, but, ok. Let's say there perhaps isn't any better comparison. Nevertheless, I still don't understand, how the simulation should work. How the agents will act? How it will be measured? How you will be able to calculate all the figures? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:28, 21 December 2018 (CET)<br /> <br /> :::: OK, I thought about how I would realize the simulation in NetLogo and I see you were right about it being too complicated. So I tried to simplify my model even more to be doable:<br /> :::: - The patches will be divided proportionally by area of the two countries, they will also be characterized by a certain portion of the initial GDP as their wealth.<br /> :::: - The turtles representing Koreans will have initial wealth (also based on the initial GDP) and they will be migrating towards one of the two states based on their inclination to migrate to the other country. This inclination will be calculated randomly based on the East-West Germany migration data and their wealth.<br /> :::: - On each tick, the simulation will determine:<br /> ::::: - if the turtle (Korean) has a job (landed one, got fired, is working, or didn't get hired) based semi-randomly on the wealth of the patch the turtle is on.<br /> ::::: - The wealth of the patch and turtle -&gt; if the turtle is employed, it gains wealth and also generates a little wealth for the patch; If unemployed, it drains the wealth of the patch a little and its wealth decreases a little.<br /> ::::: - Based on the suicide rates, employment and wealth, it will also semi-randomly decide if the person is likely to kill themselves.<br /> :::: That way we can at least simulate and see the wealth distribution, suicide rates and population distribution in a very simplified model... Does that makes sense or am I still in over my head? :) [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 19:20, 21 December 2018 (CET)<br /> <br /> ::::: To be honest, I am a bit skeptical about the results. As the assignment is based on vague assumptions, I think the results will be very general. On the other hand, I appreciate the level of preparation, which is above average. Hence, it is exceptionally '''approved''' and I hope you invest the same level of effort into the simulation itself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:52, 31 December 2018 (CET)<br /> :::::: Thank you! [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 12:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br /> <br /> Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).&lt;br/&gt;<br /> - Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).&lt;br/&gt;<br /> - Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.&lt;br/&gt;<br /> - Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.&lt;br/&gt;<br /> <br /> '''Simulation environment:''' Netlogo<br /> '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:29, 21 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Maze runner vs fire'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There will be n apartment with rooms and obstacles (tables, bed, bath...), in a random room will be a people (user selects how many), who will try to find the way out, in some other random place will be fire spreading rapidly. Agents will not be able to move through walls and obstacles. The purpose of this simulation is to simulate an escape plan for an apartment. <br /> <br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : What is the purpose of the simulation? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:30, 21 December 2018 (CET)<br /> :: to find out whether a person who is lost in a building will survive. Imagine yourself in a bulding you have never been before and a fire appears. Wouldn’t it be interesting to know If you would find your way out? [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 22:51, 21 December 2018 (CET)<br /> ::: It is just a modification of our Building escape example. Ok, it could be doable, but there must be a clear and substantial added value. Your assignment proposal must be much much more deeply elaborated. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:56, 31 December 2018 (CET)<br /> :::: Ok, edited. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 01:53, 3 January 2019 (CET)<br /> ----<br /> <br /> ==Simulation proposal [[User:qnesa01|qnesa01]] ==<br /> <br /> Topic/goal: '''Traffic simulation Argentinska street'''<br /> <br /> '''Situation:'''&lt;br/&gt;<br /> - Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br /> <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br /> <br /> <br /> '''Simulation environment''': Simprocess; SUMO (for traffic representation) <br /> <br /> '''Brief process of simulation :'''&lt;br/&gt;<br /> 1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br /> 2) Real situation simulation <br /> 3) Based on simulation of real situation will be checked the efficiency of lights changes <br /> 4) The hypothetical model of adding one more line will be created based on simulation of real situation <br /> 5) Summary<br /> <br /> : Ok, SUMO could be used for this. I just don't understand what you will simulate in SUMO, and what you will simulate in Simprocess, and why? The combination of two tools is uneasy, so there should be a good reason. However sounds interesting. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:41, 21 December 2018 (CET)<br /> : The Simprocess will be used for problem solution and SUMO for traffic representation of ready solution. Because SUMO does not have strong performance in searching solution, but very good for clear representation of ready result. Combination of two tools will be useful for this case. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 21:47, 27 December 2018 (CET)<br /> :: Sorry, I still don't understand. Please, could you describe, how the simulation will look like in Simprocess and in SUMO? What will be the data you pass from SUMO to Simprocess (or vice versa)? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:36, 31 December 2018 (CET)<br /> <br /> ::: As I have mentioned above, firstly I am going to simulate initial state (as it is now). This will be done in Simprocess and SUMO. In Simprocess will be used variables: time of lights change, distribution of cars number arriving, staying and leaving. Based on same variables, simulation in SUMO will be created to get map based view. Secondly, I would like to work with variables to answer the proposed questions. All changes will be done first in Simprocess to get optimal solution. And then based again on the result values of variables from Simprocess the map view in SUMO will be made. So, SUMO will be used for representation purpose, to show Simprocess solution on the map. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 11:27, 3 January 2019 (CET) <br /> ----<br /> <br /> == Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br /> <br /> <br /> <br /> Topic/goal: '''Survival of the apocalypse'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. &lt;br/&gt;<br /> <br /> The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br /> for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br /> the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br /> <br /> Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br /> effectivnes of the plague.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : Please, no zombie-topics! The problem is that you cannot verify such an assignment with real data. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:43, 21 December 2018 (CET)<br /> <br /> :: Alright then. My second suggestion would be simulating slime mold searching for food. (gif representing the mold [[https://www.reddit.com/r/gifs/comments/a1geie/slime_mold_searching_for_food/|here]] ) I should be able to confront that with a reality and the number of “foods” and speed of the mold could be variable. [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:36, 21 December 2018 (CET)<br /> ::: Why not, just please, elaborate the assignment into detail. What will be the agents? What will be the parameters, and why...? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:36, 31 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Medieval Battle Simulation'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.&lt;br/&gt;<br /> Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).&lt;br/&gt;<br /> Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.&lt;br/&gt;<br /> When they meet in battle, they fight to the last man who wins the dispute for his king.&lt;/br&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> It simulates medieval combat on battlefield between two sides.&lt;br/&gt;<br /> The user can select which type of units and how many will each kingdom have.&lt;br/&gt;<br /> Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br /> <br /> '''Simulation environment:''' NetLogo<br /> <br /> : I would narrow this down to testing battle strategies. Forget kingdoms. You can perform a research on some historical strategies, their pros and cons, choose some, and try to simulate them, and compare. If it sounds meaningful, please, redefine the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:48, 21 December 2018 (CET)<br /> <br /> :: The part about kingdoms was more of a story telling. The objects in NetLogo would just be the units (of different types). I envisioned them to spawn at random on their given side of the plane, but if it's necessary I could include spawning in historical formations. Would that be ok? [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 11:00, 22 December 2018 (CET)<br /> <br /> ::: The assignment is too brief to check. Please, define it in detail. Theoretically, the assignment should be the only source for you to solve it. It means that in this phase you should have thought out most of the details. Which formations you will simulate, how exactly should it look like, what will be the results, how you will evaluate it... [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:43, 31 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> At the moment we have a venue in a small town under construction which we would like to turn into a café.&lt;br/&gt; <br /> The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. &lt;br/&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.&lt;br/&gt; <br /> I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.&lt;br/&gt;<br /> &lt;br/&gt;<br /> The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br /> <br /> '''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br /> <br /> : I like the idea, just hesitate about the tool. As far as I understand, a substantial part of the whole thing is about the spatial arrangement of the café. Then Simprocess is not the first choice. Why do you want to use it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:56, 21 December 2018 (CET)<br /> <br /> ::I liked that Simprocess allowed me to work with resources. And I see the tables and chairs as a resource, as well as the working power. If the flow of people coming in is well set up, I thought, by adjusting the resources, it should be possible to find out the optional setting. Do you propose any other tool to do that? I will be greatful for other ideas to improve the solution as it is a real problem we would like to solve. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 10:21, 27 December 2018 (CET)<br /> <br /> ::: If you need to deal with spatial factors, NetLogo is definitely a better choice. If you omit the spatial characteristics, the simulation become trivial, what would not be enough for this paper. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:06, 31 December 2018 (CET)<br /> <br /> :::: Ok, I can do the simulation in NetLogo and mind the spatial characteristics. I just thought the original idea was complex enought as it would put together several rather simple simulations and tasks - like finding out the right amount of visitors based on data from people that run cafés in different places (recalculating the number of visitors based on the wealthiness of the region, based on the capacity of the venue, the number of citizens and so on). <br /> ::::I believe I can go with netLogo for simulating the venue as well, although it is not completely clear now for me, how do I find out the right ammount of seats for example?<br /> ::::I can imagine a random movement of agents coming in taking a first not-taken seat. It can also be like the agent has a capacity (it can be group of 4 or just a pair or a single person visitor) and it could take the table/seat only in case there is enough space for the whole group. There could also be a quality parameters - like grading system for the seats - how far is it from the bar, is it near the toilet and so on. So yes, that might show that some places will be more popular then others. Also the agents might have a limit under which they dont go, like if they dont find a seat with quality high enough they will leave. Now when I am writing this down I think it might work although I still have some questions. Is this something you had in mind as well or am I on completely different track? Thanks. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 11:26, 31 December 2018 (CET)<br /> <br /> ::::: All right, now I understand it better. For the purpose you mentioned, Simprocess could be fine. The only problem is that the simulation itself is rather simple. Let's stick to it, nevertheless, I will require to make it really in detail including obtaining real data and simulating all relevant aspects. '''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br /> <br /> '''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.<br /> <br /> :: I just wonder, where is the randomnes, as you want to just derive the configuration from the already operating competition. If it should be a simulation, you would have to have real daily data (for reasonable long period, a year minimum) for the demand for the flights (there and back) and also for the individual classes. Only then you are able to derive probability distributions for the simulation that would make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:31, 21 December 2018 (CET)<br /> <br /> <br /> ::: Ok, these are not available, so I will try a completely different idea:<br /> <br /> <br /> Topic/goal: '''Archiving maximum efficiency in ticket management of a small company (real-life based data)'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> A small company has support personnel, testers and developers in one office. They together solve tickets created by customers. These tickets sometimes get stuck in ticketing system, because the numbers of people working there are not perfect. Campany uses Kayako and Jira to manage tickets - from these systems, there will be a lot of real-life data to base the simulation on. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> The simulation aims to provide the ideal amount of support people, testers/analysts and developers to solve tickets as quick as possible, while there are constraints, such as company budget and lowered effectivity of too many people working on the same ticket. <br /> <br /> '''Simulation environment:''' I would like to use Simprocess to model and optimize the workflow.<br /> : All right, '''approved''', but bear in mind that a detailed simulation based on real data is expected.<br /> <br /> == Simulation proposal (xpipj04) ==<br /> <br /> Topic/goal: You are what you eat<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> Most people nowadays want to hit the gym or eat healthy to get fit and at the same time take a long high quality sleep and work effectively. All the success in life and your energy comes from the food (nutrition values) you consume a day and the regime you have. The new year is knocking the door and you want to make sure that you will plan your food and regime effectively so your life is in balance. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Based on the input you choose the simulation should be able to give you the expected levels of achieved energy, work efficiency and especially if you are about to get fit or you will drop down to be a couch potato. <br /> <br /> '''Input parameters'''&lt;br/&gt;<br /> - Food consumed - listed types of food with calculated nutrition values (protein, fat, sugar, energy) &lt;br/&gt;<br /> - Time spent at gym (kardio, workout, culturist) &lt;br/&gt;<br /> - Time spent at work - type of work (manual, manager) &lt;br/&gt;<br /> - Time spent sleeping &lt;br/&gt;<br /> <br /> '''Output'''&lt;br/&gt;<br /> - Your BMI &lt;br/&gt;<br /> - Your fitness level &lt;br/&gt;<br /> - Your work focus &lt;br/&gt;<br /> - Your energy level &lt;br/&gt;<br /> <br /> '''Where I will get the data? '''&lt;br/&gt;<br /> - My roommate is a nutrition specialist and member of huge fitness project so his advice will be the source of my dependencies between values as well as values for different types of nutrition. &lt;br/&gt;<br /> - Nutrition plans.&lt;br/&gt; <br /> <br /> '''How will I get the output?'''&lt;br/&gt;<br /> I would like to use NetLogo to be able to see both values (numbers) and also monthly progress on the display (maybe a characted running between job, eating and gym. Showing HP bar? :D).<br /> If this kind of simulation is not suitable for NetLogo then Vensim? I have no idea how would I transfer the problem into equitations which are required.<br /> <br /> :: You got it right - it is Vensim topic without any doubt. Without mathematical formulation you can not do any simulation, so you have to deal with mathematical model formulation anyway.So, if Vensim is ok with you, than we can approve it. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:20, 23 December 2018 (CET)<br /> :::: Then I will go with Vensim and try to put together something meaningful! [[User:Jan.pippal|Jan Pippal]]<br /> :::::: OK. Then '''approved'''. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 12:29, 24 December 2018 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16855 2019-01-01T22:26:48Z

<p>Xvegm00: /* Simulation proposal (Xvegm00) Simulation of semi-intelligent algae (Not approved yet) */</p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> == Simulation proposal ([[Xvegm00|Xvegm00]]) Simulation of semi-intelligent algae (Not approved yet)==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> The algae should work in two modes: food search and optimisation. The food search part involves the algae spreading in a radial pattern, until it finds a foodsource. When it succeeds, it should optimise its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well. A test of function could be for the algae to find a path through a wall with three holes and to choose the proper hole (with shortest path).<br /> <br /> Initial plan<br /> There is an initial node, where the algae begins and from which it spreads.<br /> Food is randomly distributed across the area, defined be a patch of certain color.<br /> Algae spreads as agents, leaving behind patches as temporary algae. Temporary algae have a time to live, a timeout. When foodsource is found, nutrients travel as agents back to to initial node. When nutrients visit an algae patch, they reset its timeout. Thus, only the algae which has nutrients travelling on it can survive. With a properly applied randonmness, for the travel of nutrients as well as the agents, this should optimise the pathways.<br /> <br /> ''Assignment''<br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 13:23, 30 December 2018 (CET)<br /> <br /> : Generally, it could be done, but it is necessary to elaborate this assignment into greater detail. How you will measure the results? How you can say if it was successful or not? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:21, 31 December 2018 (CET)<br /> :: Asiggnment updated. Please revise. [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 14:33, 31 December 2018 (CET)<br /> ::: Ok, now I understand it better, how the simulation works, and it is fine. The only questionmark is still the results. What is the problem you study? What is the question you solve? How do you verify, your simulation solved the problem? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:30, 31 December 2018 (CET)<br /> :::: It solves the travelling salesmen problem, which is algoritmically NP complete problem. However, it is solved through agents, which incorporates emergent intelligence, through orderly behaviour of agents. It shows, that there is a simple and low requirement solution to the problem. It can be verified, as i suggested in the assignment, by making it find the shortest path arround obstacles.[[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 23:26, 1 January 2019 (CET)<br /> <br /> == Simulation proposal ([[xkorj58|xkorj58]]) Find the thief (Not approved yet)==<br /> <br /> This simulation should be created as a space search. There shall be two types of agents - first ones, police officers looking for a thief. Second a thief (moveable or imoveable) which is about to be found. Room itself could have obstacles. Police officers will have a vision, can have memory, could be various number of them and level of shared information about searched fields.<br /> <br /> <br /> ''Assignment''<br /> <br /> Title: Find the thief<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Jiří Korčák<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 16:03, 17 December 2018 (CET)<br /> <br /> : Sorry, but I don't understand what is this simulation good for. What meaningful problem do you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:44, 31 December 2018 (CET)<br /> ::The problem should be about how memory, number of police officers and shared informations influence searching for a thief. It could have applications for AI in computer games (thats where you meet agents like this ones) or even in real life - most important thing when looking for something is: ... (when there is 20 of us looking for something, memory and shared information is not that important, etc.). [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 10:36, 31 December 2018 (CET)<br /> ::: The assignment is too brief. For the real situation, you definitely need stated where do you find relevant data to be able setting the parameters of the simulation. If you want to develop an intelligent agent for using in e.g. computer games, I am fine with that, but you need to define the environment, you need to define, how the agent should exactly behave, how his intelligence works, etc.<br /> ::: Please, choose one of the approaches (I would recommend the second one), and describe the problem really in detail. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:35, 31 December 2018 (CET)<br /> <br /> == Simulation proposal (dolj04) ==<br /> <br /> Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)&lt;br/&gt;<br /> - the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)&lt;br/&gt;<br /> - the average size is not changing that much&lt;br/&gt;<br /> - (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)&lt;br/&gt;<br /> - backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)&lt;br/&gt;<br /> - successfully processed batches older than 14 days are deleted every day&lt;br/&gt;<br /> - for precaution lets say around 5% of batches wont be processed correctly&lt;br/&gt;<br /> - those will stay on the server and will be processed every month by admins &lt;br/&gt;<br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br /> <br /> :::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br /> <br /> ::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br /> <br /> == Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''likelihood of infection with flu'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population&lt;br/&gt;<br /> <br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br /> <br /> :::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br /> :::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br /> :::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''Social and economical effects of reunification of North &amp; South Korea''' &lt;br/&gt; &lt;br/&gt;<br /> '''Definition of the problem:''' &lt;br/&gt;<br /> Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: &lt;br/&gt;<br /> - North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. &lt;br/&gt;<br /> - South Koreans will invest in North Korea area and create jobs and factories there. &lt;br/&gt; &lt;br/&gt;<br /> ''I would like to simulate:'' &lt;br/&gt;<br /> - What will be the population ratio of the two areas per square meter. &lt;br/&gt;<br /> - How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. &lt;br/&gt;<br /> - How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. &lt;br/&gt;<br /> - What will be the housing situation after the reunification. &lt;br/&gt;<br /> - How will the GDP of the new country possibly develop. &lt;br/&gt;<br /> - Possibly how much money will be needed from international help. &lt;br/&gt; &lt;br/&gt;<br /> '''Simulation environment:''' NetLogo<br /> <br /> ----<br /> : I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br /> ----<br /> :: Thank you for the feedback - I tried to break down and simplify the simulation:<br /> <br /> :: '''Agents:'''<br /> <br /> :: - North Koreans (0-14 years)<br /> :: - North Koreans (15-24 years)<br /> :: - North Koreans (25-54 years)<br /> :: - North Koreans (55-64 years)<br /> :: - North Koreans (65 years and over)<br /> <br /> :: - South Koreans (0-14 years)<br /> :: - South Koreans (15-24 years)<br /> :: - South Koreans (25-54 years)<br /> :: - South Koreans (55-64 years)<br /> :: - South Koreans (65 years and over)<br /> <br /> :: - South Korea listed companies<br /> :: - Foregin investors<br /> <br /> :: '''Parameters'''<br /> :: - North Korea GDP<br /> :: - North Korea GDP growth<br /> <br /> :: - South Korea GDP<br /> :: - South Korea GDP growth<br /> <br /> :: - North Korea area [sq. meters]<br /> :: - South Korea area [sq. meters] <br /> <br /> :: - North Korea unemployment rate<br /> :: - South Korea unemployment rate<br /> :: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br /> <br /> :: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br /> :: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br /> <br /> :: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br /> <br /> :: '''Targets of the simulation:'''<br /> <br /> :: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br /> :: - Determine unemployment in the reunited country<br /> :: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br /> :: - Determine new GDP based on above data (foreign investors being random)<br /> <br /> :: '''Data sources:'''<br /> <br /> :: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br /> <br /> :: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br /> <br /> :: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br /> <br /> :: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br /> <br /> ::: The relevance of comparison with Germany is questionable, but, ok. Let's say there perhaps isn't any better comparison. Nevertheless, I still don't understand, how the simulation should work. How the agents will act? How it will be measured? How you will be able to calculate all the figures? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:28, 21 December 2018 (CET)<br /> <br /> :::: OK, I thought about how I would realize the simulation in NetLogo and I see you were right about it being too complicated. So I tried to simplify my model even more to be doable:<br /> :::: - The patches will be divided proportionally by area of the two countries, they will also be characterized by a certain portion of the initial GDP as their wealth.<br /> :::: - The turtles representing Koreans will have initial wealth (also based on the initial GDP) and they will be migrating towards one of the two states based on their inclination to migrate to the other country. This inclination will be calculated randomly based on the East-West Germany migration data and their wealth.<br /> :::: - On each tick, the simulation will determine:<br /> ::::: - if the turtle (Korean) has a job (landed one, got fired, is working, or didn't get hired) based semi-randomly on the wealth of the patch the turtle is on.<br /> ::::: - The wealth of the patch and turtle -&gt; if the turtle is employed, it gains wealth and also generates a little wealth for the patch; If unemployed, it drains the wealth of the patch a little and its wealth decreases a little.<br /> ::::: - Based on the suicide rates, employment and wealth, it will also semi-randomly decide if the person is likely to kill themselves.<br /> :::: That way we can at least simulate and see the wealth distribution, suicide rates and population distribution in a very simplified model... Does that makes sense or am I still in over my head? :) [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 19:20, 21 December 2018 (CET)<br /> <br /> ::::: To be honest, I am a bit skeptical about the results. As the assignment is based on vague assumptions, I think the results will be very general. On the other hand, I appreciate the level of preparation, which is above average. Hence, it is exceptionally '''approved''' and I hope you invest the same level of effort into the simulation itself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:52, 31 December 2018 (CET)<br /> :::::: Thank you! [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 12:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br /> <br /> Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).&lt;br/&gt;<br /> - Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).&lt;br/&gt;<br /> - Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.&lt;br/&gt;<br /> - Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.&lt;br/&gt;<br /> <br /> '''Simulation environment:''' Netlogo<br /> '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:29, 21 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Maze runner vs fire'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There will be a maze, in one corner a person, who tries to find the way out, in the other corner will be fire spreading rapidly. Does the person survives or dies by fire? Person and fire wont be able to jump through walls.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : What is the purpose of the simulation? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:30, 21 December 2018 (CET)<br /> :: to find out whether a person who is lost in a bulding will survive. Imagine yourself in a bulding you have never been before and a fire appears. Wouldn’t it be interesting to know If you would find your way out? [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 22:51, 21 December 2018 (CET)<br /> ::: It is just a modification of our Building escape example. Ok, it could be doable, but there must be a clear and substantial added value. Your assignment proposal must be much much more deeply elaborated. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:56, 31 December 2018 (CET)<br /> ----<br /> <br /> ==Simulation proposal [[User:qnesa01|qnesa01]] ==<br /> <br /> Topic/goal: '''Traffic simulation Argentinska street'''<br /> <br /> '''Situation:'''&lt;br/&gt;<br /> - Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br /> <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br /> <br /> <br /> '''Simulation environment''': Simprocess; SUMO (for traffic representation) <br /> <br /> '''Brief process of simulation :'''&lt;br/&gt;<br /> 1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br /> 2) Real situation simulation <br /> 3) Based on simulation of real situation will be checked the efficiency of lights changes <br /> 4) The hypothetical model of adding one more line will be created based on simulation of real situation <br /> 5) Summary<br /> <br /> : Ok, SUMO could be used for this. I just don't understand what you will simulate in SUMO, and what you will simulate in Simprocess, and why? The combination of two tools is uneasy, so there should be a good reason. However sounds interesting. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:41, 21 December 2018 (CET)<br /> : The Simprocess will be used for problem solution and SUMO for traffic representation of ready solution. Because SUMO does not have strong performance in searching solution, but very good for clear representation of ready result. Combination of two tools will be useful for this case. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 21:47, 27 December 2018 (CET)<br /> :: Sorry, I still don't understand. Please, could you describe, how the simulation will look like in Simprocess and in SUMO? What will be the data you pass from SUMO to Simprocess (or vice versa)? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:36, 31 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br /> <br /> <br /> <br /> Topic/goal: '''Survival of the apocalypse'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. &lt;br/&gt;<br /> <br /> The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br /> for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br /> the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br /> <br /> Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br /> effectivnes of the plague.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : Please, no zombie-topics! The problem is that you cannot verify such an assignment with real data. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:43, 21 December 2018 (CET)<br /> <br /> :: Alright then. My second suggestion would be simulating slime mold searching for food. (gif representing the mold [[https://www.reddit.com/r/gifs/comments/a1geie/slime_mold_searching_for_food/|here]] ) I should be able to confront that with a reality and the number of “foods” and speed of the mold could be variable. [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:36, 21 December 2018 (CET)<br /> ::: Why not, just please, elaborate the assignment into detail. What will be the agents? What will be the parameters, and why...? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:36, 31 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Medieval Battle Simulation'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.&lt;br/&gt;<br /> Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).&lt;br/&gt;<br /> Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.&lt;br/&gt;<br /> When they meet in battle, they fight to the last man who wins the dispute for his king.&lt;/br&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> It simulates medieval combat on battlefield between two sides.&lt;br/&gt;<br /> The user can select which type of units and how many will each kingdom have.&lt;br/&gt;<br /> Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br /> <br /> '''Simulation environment:''' NetLogo<br /> <br /> : I would narrow this down to testing battle strategies. Forget kingdoms. You can perform a research on some historical strategies, their pros and cons, choose some, and try to simulate them, and compare. If it sounds meaningful, please, redefine the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:48, 21 December 2018 (CET)<br /> <br /> :: The part about kingdoms was more of a story telling. The objects in NetLogo would just be the units (of different types). I envisioned them to spawn at random on their given side of the plane, but if it's necessary I could include spawning in historical formations. Would that be ok? [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 11:00, 22 December 2018 (CET)<br /> <br /> ::: The assignment is too brief to check. Please, define it in detail. Theoretically, the assignment should be the only source for you to solve it. It means that in this phase you should have thought out most of the details. Which formations you will simulate, how exactly should it look like, what will be the results, how you will evaluate it... [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:43, 31 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> At the moment we have a venue in a small town under construction which we would like to turn into a café.&lt;br/&gt; <br /> The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. &lt;br/&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.&lt;br/&gt; <br /> I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.&lt;br/&gt;<br /> &lt;br/&gt;<br /> The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br /> <br /> '''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br /> <br /> : I like the idea, just hesitate about the tool. As far as I understand, a substantial part of the whole thing is about the spatial arrangement of the café. Then Simprocess is not the first choice. Why do you want to use it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:56, 21 December 2018 (CET)<br /> <br /> ::I liked that Simprocess allowed me to work with resources. And I see the tables and chairs as a resource, as well as the working power. If the flow of people coming in is well set up, I thought, by adjusting the resources, it should be possible to find out the optional setting. Do you propose any other tool to do that? I will be greatful for other ideas to improve the solution as it is a real problem we would like to solve. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 10:21, 27 December 2018 (CET)<br /> <br /> ::: If you need to deal with spatial factors, NetLogo is definitely a better choice. If you omit the spatial characteristics, the simulation become trivial, what would not be enough for this paper. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:06, 31 December 2018 (CET)<br /> <br /> :::: Ok, I can do the simulation in NetLogo and mind the spatial characteristics. I just thought the original idea was complex enought as it would put together several rather simple simulations and tasks - like finding out the right amount of visitors based on data from people that run cafés in different places (recalculating the number of visitors based on the wealthiness of the region, based on the capacity of the venue, the number of citizens and so on). <br /> ::::I believe I can go with netLogo for simulating the venue as well, although it is not completely clear now for me, how do I find out the right ammount of seats for example?<br /> ::::I can imagine a random movement of agents coming in taking a first not-taken seat. It can also be like the agent has a capacity (it can be group of 4 or just a pair or a single person visitor) and it could take the table/seat only in case there is enough space for the whole group. There could also be a quality parameters - like grading system for the seats - how far is it from the bar, is it near the toilet and so on. So yes, that might show that some places will be more popular then others. Also the agents might have a limit under which they dont go, like if they dont find a seat with quality high enough they will leave. Now when I am writing this down I think it might work although I still have some questions. Is this something you had in mind as well or am I on completely different track? Thanks. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 11:26, 31 December 2018 (CET)<br /> <br /> ::::: All right, now I understand it better. For the purpose you mentioned, Simprocess could be fine. The only problem is that the simulation itself is rather simple. Let's stick to it, nevertheless, I will require to make it really in detail including obtaining real data and simulating all relevant aspects. '''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br /> <br /> '''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.<br /> <br /> :: I just wonder, where is the randomnes, as you want to just derive the configuration from the already operating competition. If it should be a simulation, you would have to have real daily data (for reasonable long period, a year minimum) for the demand for the flights (there and back) and also for the individual classes. Only then you are able to derive probability distributions for the simulation that would make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:31, 21 December 2018 (CET)<br /> <br /> <br /> ::: Ok, these are not available, so I will try a completely different idea:<br /> <br /> <br /> Topic/goal: '''Archiving maximum efficiency in ticket management of a small company (real-life based data)'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> A small company has support personnel, testers and developers in one office. They together solve tickets created by customers. These tickets sometimes get stuck in ticketing system, because the numbers of people working there are not perfect. Campany uses Kayako and Jira to manage tickets - from these systems, there will be a lot of real-life data to base the simulation on. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> The simulation aims to provide the ideal amount of support people, testers/analysts and developers to solve tickets as quick as possible, while there are constraints, such as company budget and lowered effectivity of too many people working on the same ticket. <br /> <br /> '''Simulation environment:''' I would like to use Simprocess to model and optimize the workflow.<br /> : All right, '''approved''', but bear in mind that a detailed simulation based on real data is expected.<br /> <br /> == Simulation proposal (xpipj04) ==<br /> <br /> Topic/goal: You are what you eat<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> Most people nowadays want to hit the gym or eat healthy to get fit and at the same time take a long high quality sleep and work effectively. All the success in life and your energy comes from the food (nutrition values) you consume a day and the regime you have. The new year is knocking the door and you want to make sure that you will plan your food and regime effectively so your life is in balance. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Based on the input you choose the simulation should be able to give you the expected levels of achieved energy, work efficiency and especially if you are about to get fit or you will drop down to be a couch potato. <br /> <br /> '''Input parameters'''&lt;br/&gt;<br /> - Food consumed - listed types of food with calculated nutrition values (protein, fat, sugar, energy) &lt;br/&gt;<br /> - Time spent at gym (kardio, workout, culturist) &lt;br/&gt;<br /> - Time spent at work - type of work (manual, manager) &lt;br/&gt;<br /> - Time spent sleeping &lt;br/&gt;<br /> <br /> '''Output'''&lt;br/&gt;<br /> - Your BMI &lt;br/&gt;<br /> - Your fitness level &lt;br/&gt;<br /> - Your work focus &lt;br/&gt;<br /> - Your energy level &lt;br/&gt;<br /> <br /> '''Where I will get the data? '''&lt;br/&gt;<br /> - My roommate is a nutrition specialist and member of huge fitness project so his advice will be the source of my dependencies between values as well as values for different types of nutrition. &lt;br/&gt;<br /> - Nutrition plans.&lt;br/&gt; <br /> <br /> '''How will I get the output?'''&lt;br/&gt;<br /> I would like to use NetLogo to be able to see both values (numbers) and also monthly progress on the display (maybe a characted running between job, eating and gym. Showing HP bar? :D).<br /> If this kind of simulation is not suitable for NetLogo then Vensim? I have no idea how would I transfer the problem into equitations which are required.<br /> <br /> :: You got it right - it is Vensim topic without any doubt. Without mathematical formulation you can not do any simulation, so you have to deal with mathematical model formulation anyway.So, if Vensim is ok with you, than we can approve it. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:20, 23 December 2018 (CET)<br /> :::: Then I will go with Vensim and try to put together something meaningful! [[User:Jan.pippal|Jan Pippal]]<br /> :::::: OK. Then '''approved'''. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 12:29, 24 December 2018 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16854 2019-01-01T22:26:15Z

<p>Xvegm00: /* Simulation proposal (Xvegm00) Simulation of semi-intelligent algae (Not approved yet) */</p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> == Simulation proposal ([[Xvegm00|Xvegm00]]) Simulation of semi-intelligent algae (Not approved yet)==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> The algae should work in two modes: food search and optimisation. The food search part involves the algae spreading in a radial pattern, until it finds a foodsource. When it succeeds, it should optimise its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well. A test of function could be for the algae to find a path through a wall with three holes and to choose the proper hole (with shortest path).<br /> <br /> Initial plan<br /> There is an initial node, where the algae begins and from which it spreads.<br /> Food is randomly distributed across the area, defined be a patch of certain color.<br /> Algae spreads as agents, leaving behind patches as temporary algae. Temporary algae have a time to live, a timeout. When foodsource is found, nutrients travel as agents back to to initial node. When nutrients visit an algae patch, they reset its timeout. Thus, only the algae which has nutrients travelling on it can survive. With a properly applied randonmness, for the travel of nutrients as well as the agents, this should optimise the pathways.<br /> <br /> ''Assignment''<br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 13:23, 30 December 2018 (CET)<br /> <br /> : Generally, it could be done, but it is necessary to elaborate this assignment into greater detail. How you will measure the results? How you can say if it was successful or not? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:21, 31 December 2018 (CET)<br /> :: Asiggnment updated. Please revise. [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 14:33, 31 December 2018 (CET)<br /> ::: Ok, now I understand it better, how the simulation works, and it is fine. The only questionmark is still the results. What is the problem you study? What is the question you solve? How do you verify, your simulation solved the problem? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:30, 31 December 2018 (CET)<br /> :::: It solves the travelling salesmen problem, which is algoritmically NP complete problem. However, it is solved through agents, which incorporates emergent intelligence, through orderly behaviour of agents. It shows, that there is a simple and low requirement solution to the problem. It can be verified, as i suggested in the assignment, by making it find the shortest path arround obstacles.<br /> [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 23:26, 1 January 2019 (CET)<br /> <br /> == Simulation proposal ([[xkorj58|xkorj58]]) Find the thief (Not approved yet)==<br /> <br /> This simulation should be created as a space search. There shall be two types of agents - first ones, police officers looking for a thief. Second a thief (moveable or imoveable) which is about to be found. Room itself could have obstacles. Police officers will have a vision, can have memory, could be various number of them and level of shared information about searched fields.<br /> <br /> <br /> ''Assignment''<br /> <br /> Title: Find the thief<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Jiří Korčák<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 16:03, 17 December 2018 (CET)<br /> <br /> : Sorry, but I don't understand what is this simulation good for. What meaningful problem do you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:44, 31 December 2018 (CET)<br /> ::The problem should be about how memory, number of police officers and shared informations influence searching for a thief. It could have applications for AI in computer games (thats where you meet agents like this ones) or even in real life - most important thing when looking for something is: ... (when there is 20 of us looking for something, memory and shared information is not that important, etc.). [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 10:36, 31 December 2018 (CET)<br /> ::: The assignment is too brief. For the real situation, you definitely need stated where do you find relevant data to be able setting the parameters of the simulation. If you want to develop an intelligent agent for using in e.g. computer games, I am fine with that, but you need to define the environment, you need to define, how the agent should exactly behave, how his intelligence works, etc.<br /> ::: Please, choose one of the approaches (I would recommend the second one), and describe the problem really in detail. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:35, 31 December 2018 (CET)<br /> <br /> == Simulation proposal (dolj04) ==<br /> <br /> Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)&lt;br/&gt;<br /> - the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)&lt;br/&gt;<br /> - the average size is not changing that much&lt;br/&gt;<br /> - (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)&lt;br/&gt;<br /> - backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)&lt;br/&gt;<br /> - successfully processed batches older than 14 days are deleted every day&lt;br/&gt;<br /> - for precaution lets say around 5% of batches wont be processed correctly&lt;br/&gt;<br /> - those will stay on the server and will be processed every month by admins &lt;br/&gt;<br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br /> <br /> :::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br /> <br /> ::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br /> <br /> == Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''likelihood of infection with flu'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population&lt;br/&gt;<br /> <br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br /> <br /> :::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br /> :::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br /> :::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''Social and economical effects of reunification of North &amp; South Korea''' &lt;br/&gt; &lt;br/&gt;<br /> '''Definition of the problem:''' &lt;br/&gt;<br /> Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: &lt;br/&gt;<br /> - North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. &lt;br/&gt;<br /> - South Koreans will invest in North Korea area and create jobs and factories there. &lt;br/&gt; &lt;br/&gt;<br /> ''I would like to simulate:'' &lt;br/&gt;<br /> - What will be the population ratio of the two areas per square meter. &lt;br/&gt;<br /> - How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. &lt;br/&gt;<br /> - How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. &lt;br/&gt;<br /> - What will be the housing situation after the reunification. &lt;br/&gt;<br /> - How will the GDP of the new country possibly develop. &lt;br/&gt;<br /> - Possibly how much money will be needed from international help. &lt;br/&gt; &lt;br/&gt;<br /> '''Simulation environment:''' NetLogo<br /> <br /> ----<br /> : I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br /> ----<br /> :: Thank you for the feedback - I tried to break down and simplify the simulation:<br /> <br /> :: '''Agents:'''<br /> <br /> :: - North Koreans (0-14 years)<br /> :: - North Koreans (15-24 years)<br /> :: - North Koreans (25-54 years)<br /> :: - North Koreans (55-64 years)<br /> :: - North Koreans (65 years and over)<br /> <br /> :: - South Koreans (0-14 years)<br /> :: - South Koreans (15-24 years)<br /> :: - South Koreans (25-54 years)<br /> :: - South Koreans (55-64 years)<br /> :: - South Koreans (65 years and over)<br /> <br /> :: - South Korea listed companies<br /> :: - Foregin investors<br /> <br /> :: '''Parameters'''<br /> :: - North Korea GDP<br /> :: - North Korea GDP growth<br /> <br /> :: - South Korea GDP<br /> :: - South Korea GDP growth<br /> <br /> :: - North Korea area [sq. meters]<br /> :: - South Korea area [sq. meters] <br /> <br /> :: - North Korea unemployment rate<br /> :: - South Korea unemployment rate<br /> :: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br /> <br /> :: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br /> :: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br /> <br /> :: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br /> <br /> :: '''Targets of the simulation:'''<br /> <br /> :: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br /> :: - Determine unemployment in the reunited country<br /> :: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br /> :: - Determine new GDP based on above data (foreign investors being random)<br /> <br /> :: '''Data sources:'''<br /> <br /> :: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br /> <br /> :: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br /> <br /> :: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br /> <br /> :: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br /> <br /> ::: The relevance of comparison with Germany is questionable, but, ok. Let's say there perhaps isn't any better comparison. Nevertheless, I still don't understand, how the simulation should work. How the agents will act? How it will be measured? How you will be able to calculate all the figures? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:28, 21 December 2018 (CET)<br /> <br /> :::: OK, I thought about how I would realize the simulation in NetLogo and I see you were right about it being too complicated. So I tried to simplify my model even more to be doable:<br /> :::: - The patches will be divided proportionally by area of the two countries, they will also be characterized by a certain portion of the initial GDP as their wealth.<br /> :::: - The turtles representing Koreans will have initial wealth (also based on the initial GDP) and they will be migrating towards one of the two states based on their inclination to migrate to the other country. This inclination will be calculated randomly based on the East-West Germany migration data and their wealth.<br /> :::: - On each tick, the simulation will determine:<br /> ::::: - if the turtle (Korean) has a job (landed one, got fired, is working, or didn't get hired) based semi-randomly on the wealth of the patch the turtle is on.<br /> ::::: - The wealth of the patch and turtle -&gt; if the turtle is employed, it gains wealth and also generates a little wealth for the patch; If unemployed, it drains the wealth of the patch a little and its wealth decreases a little.<br /> ::::: - Based on the suicide rates, employment and wealth, it will also semi-randomly decide if the person is likely to kill themselves.<br /> :::: That way we can at least simulate and see the wealth distribution, suicide rates and population distribution in a very simplified model... Does that makes sense or am I still in over my head? :) [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 19:20, 21 December 2018 (CET)<br /> <br /> ::::: To be honest, I am a bit skeptical about the results. As the assignment is based on vague assumptions, I think the results will be very general. On the other hand, I appreciate the level of preparation, which is above average. Hence, it is exceptionally '''approved''' and I hope you invest the same level of effort into the simulation itself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:52, 31 December 2018 (CET)<br /> :::::: Thank you! [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 12:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br /> <br /> Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).&lt;br/&gt;<br /> - Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).&lt;br/&gt;<br /> - Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.&lt;br/&gt;<br /> - Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.&lt;br/&gt;<br /> <br /> '''Simulation environment:''' Netlogo<br /> '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:29, 21 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Maze runner vs fire'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There will be a maze, in one corner a person, who tries to find the way out, in the other corner will be fire spreading rapidly. Does the person survives or dies by fire? Person and fire wont be able to jump through walls.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : What is the purpose of the simulation? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:30, 21 December 2018 (CET)<br /> :: to find out whether a person who is lost in a bulding will survive. Imagine yourself in a bulding you have never been before and a fire appears. Wouldn’t it be interesting to know If you would find your way out? [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 22:51, 21 December 2018 (CET)<br /> ::: It is just a modification of our Building escape example. Ok, it could be doable, but there must be a clear and substantial added value. Your assignment proposal must be much much more deeply elaborated. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:56, 31 December 2018 (CET)<br /> ----<br /> <br /> ==Simulation proposal [[User:qnesa01|qnesa01]] ==<br /> <br /> Topic/goal: '''Traffic simulation Argentinska street'''<br /> <br /> '''Situation:'''&lt;br/&gt;<br /> - Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br /> <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br /> <br /> <br /> '''Simulation environment''': Simprocess; SUMO (for traffic representation) <br /> <br /> '''Brief process of simulation :'''&lt;br/&gt;<br /> 1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br /> 2) Real situation simulation <br /> 3) Based on simulation of real situation will be checked the efficiency of lights changes <br /> 4) The hypothetical model of adding one more line will be created based on simulation of real situation <br /> 5) Summary<br /> <br /> : Ok, SUMO could be used for this. I just don't understand what you will simulate in SUMO, and what you will simulate in Simprocess, and why? The combination of two tools is uneasy, so there should be a good reason. However sounds interesting. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:41, 21 December 2018 (CET)<br /> : The Simprocess will be used for problem solution and SUMO for traffic representation of ready solution. Because SUMO does not have strong performance in searching solution, but very good for clear representation of ready result. Combination of two tools will be useful for this case. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 21:47, 27 December 2018 (CET)<br /> :: Sorry, I still don't understand. Please, could you describe, how the simulation will look like in Simprocess and in SUMO? What will be the data you pass from SUMO to Simprocess (or vice versa)? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:36, 31 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br /> <br /> <br /> <br /> Topic/goal: '''Survival of the apocalypse'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. &lt;br/&gt;<br /> <br /> The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br /> for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br /> the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br /> <br /> Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br /> effectivnes of the plague.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : Please, no zombie-topics! The problem is that you cannot verify such an assignment with real data. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:43, 21 December 2018 (CET)<br /> <br /> :: Alright then. My second suggestion would be simulating slime mold searching for food. (gif representing the mold [[https://www.reddit.com/r/gifs/comments/a1geie/slime_mold_searching_for_food/|here]] ) I should be able to confront that with a reality and the number of “foods” and speed of the mold could be variable. [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:36, 21 December 2018 (CET)<br /> ::: Why not, just please, elaborate the assignment into detail. What will be the agents? What will be the parameters, and why...? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:36, 31 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Medieval Battle Simulation'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.&lt;br/&gt;<br /> Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).&lt;br/&gt;<br /> Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.&lt;br/&gt;<br /> When they meet in battle, they fight to the last man who wins the dispute for his king.&lt;/br&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> It simulates medieval combat on battlefield between two sides.&lt;br/&gt;<br /> The user can select which type of units and how many will each kingdom have.&lt;br/&gt;<br /> Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br /> <br /> '''Simulation environment:''' NetLogo<br /> <br /> : I would narrow this down to testing battle strategies. Forget kingdoms. You can perform a research on some historical strategies, their pros and cons, choose some, and try to simulate them, and compare. If it sounds meaningful, please, redefine the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:48, 21 December 2018 (CET)<br /> <br /> :: The part about kingdoms was more of a story telling. The objects in NetLogo would just be the units (of different types). I envisioned them to spawn at random on their given side of the plane, but if it's necessary I could include spawning in historical formations. Would that be ok? [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 11:00, 22 December 2018 (CET)<br /> <br /> ::: The assignment is too brief to check. Please, define it in detail. Theoretically, the assignment should be the only source for you to solve it. It means that in this phase you should have thought out most of the details. Which formations you will simulate, how exactly should it look like, what will be the results, how you will evaluate it... [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:43, 31 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> At the moment we have a venue in a small town under construction which we would like to turn into a café.&lt;br/&gt; <br /> The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. &lt;br/&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.&lt;br/&gt; <br /> I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.&lt;br/&gt;<br /> &lt;br/&gt;<br /> The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br /> <br /> '''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br /> <br /> : I like the idea, just hesitate about the tool. As far as I understand, a substantial part of the whole thing is about the spatial arrangement of the café. Then Simprocess is not the first choice. Why do you want to use it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:56, 21 December 2018 (CET)<br /> <br /> ::I liked that Simprocess allowed me to work with resources. And I see the tables and chairs as a resource, as well as the working power. If the flow of people coming in is well set up, I thought, by adjusting the resources, it should be possible to find out the optional setting. Do you propose any other tool to do that? I will be greatful for other ideas to improve the solution as it is a real problem we would like to solve. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 10:21, 27 December 2018 (CET)<br /> <br /> ::: If you need to deal with spatial factors, NetLogo is definitely a better choice. If you omit the spatial characteristics, the simulation become trivial, what would not be enough for this paper. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:06, 31 December 2018 (CET)<br /> <br /> :::: Ok, I can do the simulation in NetLogo and mind the spatial characteristics. I just thought the original idea was complex enought as it would put together several rather simple simulations and tasks - like finding out the right amount of visitors based on data from people that run cafés in different places (recalculating the number of visitors based on the wealthiness of the region, based on the capacity of the venue, the number of citizens and so on). <br /> ::::I believe I can go with netLogo for simulating the venue as well, although it is not completely clear now for me, how do I find out the right ammount of seats for example?<br /> ::::I can imagine a random movement of agents coming in taking a first not-taken seat. It can also be like the agent has a capacity (it can be group of 4 or just a pair or a single person visitor) and it could take the table/seat only in case there is enough space for the whole group. There could also be a quality parameters - like grading system for the seats - how far is it from the bar, is it near the toilet and so on. So yes, that might show that some places will be more popular then others. Also the agents might have a limit under which they dont go, like if they dont find a seat with quality high enough they will leave. Now when I am writing this down I think it might work although I still have some questions. Is this something you had in mind as well or am I on completely different track? Thanks. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 11:26, 31 December 2018 (CET)<br /> <br /> ::::: All right, now I understand it better. For the purpose you mentioned, Simprocess could be fine. The only problem is that the simulation itself is rather simple. Let's stick to it, nevertheless, I will require to make it really in detail including obtaining real data and simulating all relevant aspects. '''Approved.''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 21:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br /> <br /> '''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.<br /> <br /> :: I just wonder, where is the randomnes, as you want to just derive the configuration from the already operating competition. If it should be a simulation, you would have to have real daily data (for reasonable long period, a year minimum) for the demand for the flights (there and back) and also for the individual classes. Only then you are able to derive probability distributions for the simulation that would make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:31, 21 December 2018 (CET)<br /> <br /> <br /> ::: Ok, these are not available, so I will try a completely different idea:<br /> <br /> <br /> Topic/goal: '''Archiving maximum efficiency in ticket management of a small company (real-life based data)'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> A small company has support personnel, testers and developers in one office. They together solve tickets created by customers. These tickets sometimes get stuck in ticketing system, because the numbers of people working there are not perfect. Campany uses Kayako and Jira to manage tickets - from these systems, there will be a lot of real-life data to base the simulation on. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> The simulation aims to provide the ideal amount of support people, testers/analysts and developers to solve tickets as quick as possible, while there are constraints, such as company budget and lowered effectivity of too many people working on the same ticket. <br /> <br /> '''Simulation environment:''' I would like to use Simprocess to model and optimize the workflow.<br /> : All right, '''approved''', but bear in mind that a detailed simulation based on real data is expected.<br /> <br /> == Simulation proposal (xpipj04) ==<br /> <br /> Topic/goal: You are what you eat<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> Most people nowadays want to hit the gym or eat healthy to get fit and at the same time take a long high quality sleep and work effectively. All the success in life and your energy comes from the food (nutrition values) you consume a day and the regime you have. The new year is knocking the door and you want to make sure that you will plan your food and regime effectively so your life is in balance. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Based on the input you choose the simulation should be able to give you the expected levels of achieved energy, work efficiency and especially if you are about to get fit or you will drop down to be a couch potato. <br /> <br /> '''Input parameters'''&lt;br/&gt;<br /> - Food consumed - listed types of food with calculated nutrition values (protein, fat, sugar, energy) &lt;br/&gt;<br /> - Time spent at gym (kardio, workout, culturist) &lt;br/&gt;<br /> - Time spent at work - type of work (manual, manager) &lt;br/&gt;<br /> - Time spent sleeping &lt;br/&gt;<br /> <br /> '''Output'''&lt;br/&gt;<br /> - Your BMI &lt;br/&gt;<br /> - Your fitness level &lt;br/&gt;<br /> - Your work focus &lt;br/&gt;<br /> - Your energy level &lt;br/&gt;<br /> <br /> '''Where I will get the data? '''&lt;br/&gt;<br /> - My roommate is a nutrition specialist and member of huge fitness project so his advice will be the source of my dependencies between values as well as values for different types of nutrition. &lt;br/&gt;<br /> - Nutrition plans.&lt;br/&gt; <br /> <br /> '''How will I get the output?'''&lt;br/&gt;<br /> I would like to use NetLogo to be able to see both values (numbers) and also monthly progress on the display (maybe a characted running between job, eating and gym. Showing HP bar? :D).<br /> If this kind of simulation is not suitable for NetLogo then Vensim? I have no idea how would I transfer the problem into equitations which are required.<br /> <br /> :: You got it right - it is Vensim topic without any doubt. Without mathematical formulation you can not do any simulation, so you have to deal with mathematical model formulation anyway.So, if Vensim is ok with you, than we can approve it. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:20, 23 December 2018 (CET)<br /> :::: Then I will go with Vensim and try to put together something meaningful! [[User:Jan.pippal|Jan Pippal]]<br /> :::::: OK. Then '''approved'''. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 12:29, 24 December 2018 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16833 2018-12-31T13:33:01Z

<p>Xvegm00: /* Simulation proposal (Xvegm00) Simulation of semi-intelligent algae (Not approved yet) */</p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> == Simulation proposal ([[Xvegm00|Xvegm00]]) Simulation of semi-intelligent algae (Not approved yet)==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> The algae should work in two modes: food search and optimisation. The food search part involves the algae spreading in a radial pattern, until it finds a foodsource. When it succeeds, it should optimise its pathways to allow for fastest nutrient transport.<br /> The goal is for the algae to be able to find the shortest path to food source (and optimize - destroy/recontruct exisiting pathways). It should be able to work around obstacles as well. A test of function could be for the algae to find a path through a wall with three holes and to choose the proper hole (with shortest path).<br /> <br /> Initial plan<br /> There is an initial node, where the algae begins and from which it spreads.<br /> Food is randomly distributed across the area, defined be a patch of certain color.<br /> Algae spreads as agents, leaving behind patches as temporary algae. Temporary algae have a time to live, a timeout. When foodsource is found, nutrients travel as agents back to to initial node. When nutrients visit an algae patch, they reset its timeout. Thus, only the algae which has nutrients travelling on it can survive. With a properly applied randonmness, for the travel of nutrients as well as the agents, this should optimise the pathways.<br /> <br /> ''Assignment''<br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 13:23, 30 December 2018 (CET)<br /> <br /> : Generally, it could be done, but it is necessary to elaborate this assignment into greater detail. How you will measure the results? How you can say if it was successful or not? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:21, 31 December 2018 (CET)<br /> :: Asiggnment updated. Please revise. [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 14:33, 31 December 2018 (CET)<br /> <br /> == Simulation proposal ([[xkorj58|xkorj58]]) Find the thief (Not approved yet)==<br /> <br /> This simulation should be created as a space search. There shall be two types of agents - first ones, police officers looking for a thief. Second a thief (moveable or imoveable) which is about to be found. Room itself could have obstacles. Police officers will have a vision, can have memory, could be various number of them and level of shared information about searched fields.<br /> <br /> <br /> ''Assignment''<br /> <br /> Title: Find the thief<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Jiří Korčák<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 16:03, 17 December 2018 (CET)<br /> <br /> : Sorry, but I don't understand what is this simulation good for. What meaningful problem do you solve? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:44, 31 December 2018 (CET)<br /> ::The problem should be about how memory, number of police officers and shared informations influence searching for a thief. It could have applications for AI in computer games (thats where you meet agents like this ones) or even in real life - most important thing when looking for something is: ... (when there is 20 of us looking for something, memory and shared information is not that important, etc.). [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 10:36, 31 December 2018 (CET)<br /> <br /> == Simulation proposal (dolj04) ==<br /> <br /> Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)&lt;br/&gt;<br /> - the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)&lt;br/&gt;<br /> - the average size is not changing that much&lt;br/&gt;<br /> - (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)&lt;br/&gt;<br /> - backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)&lt;br/&gt;<br /> - successfully processed batches older than 14 days are deleted every day&lt;br/&gt;<br /> - for precaution lets say around 5% of batches wont be processed correctly&lt;br/&gt;<br /> - those will stay on the server and will be processed every month by admins &lt;br/&gt;<br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br /> <br /> :::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br /> <br /> ::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br /> <br /> == Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''likelihood of infection with flu'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population&lt;br/&gt;<br /> <br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br /> <br /> :::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br /> :::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br /> :::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''Social and economical effects of reunification of North &amp; South Korea''' &lt;br/&gt; &lt;br/&gt;<br /> '''Definition of the problem:''' &lt;br/&gt;<br /> Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: &lt;br/&gt;<br /> - North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. &lt;br/&gt;<br /> - South Koreans will invest in North Korea area and create jobs and factories there. &lt;br/&gt; &lt;br/&gt;<br /> ''I would like to simulate:'' &lt;br/&gt;<br /> - What will be the population ratio of the two areas per square meter. &lt;br/&gt;<br /> - How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. &lt;br/&gt;<br /> - How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. &lt;br/&gt;<br /> - What will be the housing situation after the reunification. &lt;br/&gt;<br /> - How will the GDP of the new country possibly develop. &lt;br/&gt;<br /> - Possibly how much money will be needed from international help. &lt;br/&gt; &lt;br/&gt;<br /> '''Simulation environment:''' NetLogo<br /> <br /> ----<br /> : I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br /> ----<br /> :: Thank you for the feedback - I tried to break down and simplify the simulation:<br /> <br /> :: '''Agents:'''<br /> <br /> :: - North Koreans (0-14 years)<br /> :: - North Koreans (15-24 years)<br /> :: - North Koreans (25-54 years)<br /> :: - North Koreans (55-64 years)<br /> :: - North Koreans (65 years and over)<br /> <br /> :: - South Koreans (0-14 years)<br /> :: - South Koreans (15-24 years)<br /> :: - South Koreans (25-54 years)<br /> :: - South Koreans (55-64 years)<br /> :: - South Koreans (65 years and over)<br /> <br /> :: - South Korea listed companies<br /> :: - Foregin investors<br /> <br /> :: '''Parameters'''<br /> :: - North Korea GDP<br /> :: - North Korea GDP growth<br /> <br /> :: - South Korea GDP<br /> :: - South Korea GDP growth<br /> <br /> :: - North Korea area [sq. meters]<br /> :: - South Korea area [sq. meters] <br /> <br /> :: - North Korea unemployment rate<br /> :: - South Korea unemployment rate<br /> :: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br /> <br /> :: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br /> :: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br /> <br /> :: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br /> <br /> :: '''Targets of the simulation:'''<br /> <br /> :: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br /> :: - Determine unemployment in the reunited country<br /> :: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br /> :: - Determine new GDP based on above data (foreign investors being random)<br /> <br /> :: '''Data sources:'''<br /> <br /> :: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br /> <br /> :: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br /> <br /> :: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br /> <br /> :: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br /> <br /> ::: The relevance of comparison with Germany is questionable, but, ok. Let's say there perhaps isn't any better comparison. Nevertheless, I still don't understand, how the simulation should work. How the agents will act? How it will be measured? How you will be able to calculate all the figures? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:28, 21 December 2018 (CET)<br /> <br /> :::: OK, I thought about how I would realize the simulation in NetLogo and I see you were right about it being too complicated. So I tried to simplify my model even more to be doable:<br /> :::: - The patches will be divided proportionally by area of the two countries, they will also be characterized by a certain portion of the initial GDP as their wealth.<br /> :::: - The turtles representing Koreans will have initial wealth (also based on the initial GDP) and they will be migrating towards one of the two states based on their inclination to migrate to the other country. This inclination will be calculated randomly based on the East-West Germany migration data and their wealth.<br /> :::: - On each tick, the simulation will determine:<br /> ::::: - if the turtle (Korean) has a job (landed one, got fired, is working, or didn't get hired) based semi-randomly on the wealth of the patch the turtle is on.<br /> ::::: - The wealth of the patch and turtle -&gt; if the turtle is employed, it gains wealth and also generates a little wealth for the patch; If unemployed, it drains the wealth of the patch a little and its wealth decreases a little.<br /> ::::: - Based on the suicide rates, employment and wealth, it will also semi-randomly decide if the person is likely to kill themselves.<br /> :::: That way we can at least simulate and see the wealth distribution, suicide rates and population distribution in a very simplified model... Does that makes sense or am I still in over my head? :) [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 19:20, 21 December 2018 (CET)<br /> <br /> ::::: To be honest, I am a bit skeptical about the results. As the assignment is based on vague assumptions, I think the results will be very general. On the other hand, I appreciate the level of preparation, which is above average. Hence, it is exceptionally '''approved''' and I hope you invest the same level of effort into the simulation itself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:52, 31 December 2018 (CET)<br /> :::::: Thank you! [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 12:58, 31 December 2018 (CET)<br /> <br /> == Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br /> <br /> Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).&lt;br/&gt;<br /> - Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).&lt;br/&gt;<br /> - Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.&lt;br/&gt;<br /> - Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.&lt;br/&gt;<br /> <br /> '''Simulation environment:''' Netlogo<br /> '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:29, 21 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Maze runner vs fire'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There will be a maze, in one corner a person, who tries to find the way out, in the other corner will be fire spreading rapidly. Does the person survives or dies by fire? Person and fire wont be able to jump through walls.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : What is the purpose of the simulation? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:30, 21 December 2018 (CET)<br /> :: to find out whether a person who is lost in a bulding will survive. Imagine yourself in a bulding you have never been before and a fire appears. Wouldn’t it be interesting to know If you would find your way out? [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 22:51, 21 December 2018 (CET)<br /> ::: It is just a modification of our Building escape example. Ok, it could be doable, but there must be a clear and substantial added value. Your assignment proposal must be much much more deeply elaborated. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 01:56, 31 December 2018 (CET)<br /> ----<br /> <br /> ==Simulation proposal [[User:qnesa01|qnesa01]] ==<br /> <br /> Topic/goal: '''Traffic simulation Argentinska street'''<br /> <br /> '''Situation:'''&lt;br/&gt;<br /> - Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br /> <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br /> <br /> <br /> '''Simulation environment''': Simprocess; SUMO (for traffic representation) <br /> <br /> '''Brief process of simulation :'''&lt;br/&gt;<br /> 1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br /> 2) Real situation simulation <br /> 3) Based on simulation of real situation will be checked the efficiency of lights changes <br /> 4) The hypothetical model of adding one more line will be created based on simulation of real situation <br /> 5) Summary<br /> <br /> : Ok, SUMO could be used for this. I just don't understand what you will simulate in SUMO, and what you will simulate in Simprocess, and why? The combination of two tools is uneasy, so there should be a good reason. However sounds interesting. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:41, 21 December 2018 (CET)<br /> : The Simprocess will be used for problem solution and SUMO for traffic representation of ready solution. Because SUMO does not have strong performance in searching solution, but very good for clear representation of ready result. Combination of two tools will be useful for this case. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 21:47, 27 December 2018 (CET)<br /> :: Sorry, I still don't understand. Please, could you describe, how the simulation will look like in Simprocess and in SUMO? What will be the data you pass from SUMO to Simprocess (or vice versa)?<br /> ----<br /> <br /> == Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br /> <br /> <br /> <br /> Topic/goal: '''Survival of the apocalypse'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. &lt;br/&gt;<br /> <br /> The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br /> for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br /> the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br /> <br /> Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br /> effectivnes of the plague.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : Please, no zombie-topics! The problem is that you cannot verify such an assignment with real data. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:43, 21 December 2018 (CET)<br /> <br /> :: Alright then. My second suggestion would be simulating slime mold searching for food. (gif representing the mold [[https://www.reddit.com/r/gifs/comments/a1geie/slime_mold_searching_for_food/|here]] ) I should be able to confront that with a reality and the number of “foods” and speed of the mold could be variable. [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:36, 21 December 2018 (CET)<br /> ::: Why not, just please, elaborate the assignment into detail. What will be the agents? What will be the parameters, and why...?<br /> ----<br /> <br /> == Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Medieval Battle Simulation'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.&lt;br/&gt;<br /> Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).&lt;br/&gt;<br /> Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.&lt;br/&gt;<br /> When they meet in battle, they fight to the last man who wins the dispute for his king.&lt;/br&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> It simulates medieval combat on battlefield between two sides.&lt;br/&gt;<br /> The user can select which type of units and how many will each kingdom have.&lt;br/&gt;<br /> Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br /> <br /> '''Simulation environment:''' NetLogo<br /> <br /> : I would narrow this down to testing battle strategies. Forget kingdoms. You can perform a research on some historical strategies, their pros and cons, choose some, and try to simulate them, and compare. If it sounds meaningful, please, redefine the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:48, 21 December 2018 (CET)<br /> <br /> :: The part about kingdoms was more of a story telling. The objects in NetLogo would just be the units (of different types). I envisioned them to spawn at random on their given side of the plane, but if it's necessary I could include spawning in historical formations. Would that be ok? [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 11:00, 22 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> At the moment we have a venue in a small town under construction which we would like to turn into a café.&lt;br/&gt; <br /> The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. &lt;br/&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.&lt;br/&gt; <br /> I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.&lt;br/&gt;<br /> &lt;br/&gt;<br /> The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br /> <br /> '''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br /> <br /> : I like the idea, just hesitate about the tool. As far as I understand, a substantial part of the whole thing is about the spatial arrangement of the café. Then Simprocess is not the first choice. Why do you want to use it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:56, 21 December 2018 (CET)<br /> <br /> ::I liked that Simprocess allowed me to work with resources. And I see the tables and chairs as a resource, as well as the working power. If the flow of people coming in is well set up, I thought, by adjusting the resources, it should be possible to find out the optional setting. Do you propose any other tool to do that? I will be greatful for other ideas to improve the solution as it is a real problem we would like to solve. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 10:21, 27 December 2018 (CET)<br /> <br /> ::: If you need to deal with spatial factors, NetLogo is definitely a better choice. If you omit the spatial characteristics, the simulation become trivial, what would not be enough for this paper. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:06, 31 December 2018 (CET)<br /> <br /> :::: Ok, I can do the simulation in NetLogo and mind the spatial characteristics. I just thought the original idea was complex enought as it would put together several rather simple simulations and tasks - like finding out the right amount of visitors based on data from people that run cafés in different places (recalculating the number of visitors based on the wealthiness of the region, based on the capacity of the venue, the number of citizens and so on). <br /> ::::I believe I can go with netLogo for simulating the venue as well, although it is not completely clear now for me, how do I find out the right ammount of seats for example?<br /> ::::I can imagine a random movement of agents coming in taking a first not-taken seat. It can also be like the agent has a capacity (it can be group of 4 or just a pair or a single person visitor) and it could take the table/seat only in case there is enough space for the whole group. There could also be a quality parameters - like grading system for the seats - how far is it from the bar, is it near the toilet and so on. So yes, that might show that some places will be more popular then others. Also the agents might have a limit under which they dont go, like if they dont find a seat with quality high enough they will leave. Now when I am writing this down I think it might work although I still have some questions. Is this something you had in mind as well or am I on completely different track? Thanks. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 11:26, 31 December 2018 (CET)<br /> <br /> == Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br /> <br /> '''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.<br /> <br /> :: I just wonder, where is the randomnes, as you want to just derive the configuration from the already operating competition. If it should be a simulation, you would have to have real daily data (for reasonable long period, a year minimum) for the demand for the flights (there and back) and also for the individual classes. Only then you are able to derive probability distributions for the simulation that would make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:31, 21 December 2018 (CET)<br /> <br /> <br /> == Simulation proposal (xpipj04) ==<br /> <br /> Topic/goal: You are what you eat<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> Most people nowadays want to hit the gym or eat healthy to get fit and at the same time take a long high quality sleep and work effectively. All the success in life and your energy comes from the food (nutrition values) you consume a day and the regime you have. The new year is knocking the door and you want to make sure that you will plan your food and regime effectively so your life is in balance. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Based on the input you choose the simulation should be able to give you the expected levels of achieved energy, work efficiency and especially if you are about to get fit or you will drop down to be a couch potato. <br /> <br /> '''Input parameters'''&lt;br/&gt;<br /> - Food consumed - listed types of food with calculated nutrition values (protein, fat, sugar, energy) &lt;br/&gt;<br /> - Time spent at gym (kardio, workout, culturist) &lt;br/&gt;<br /> - Time spent at work - type of work (manual, manager) &lt;br/&gt;<br /> - Time spent sleeping &lt;br/&gt;<br /> <br /> '''Output'''&lt;br/&gt;<br /> - Your BMI &lt;br/&gt;<br /> - Your fitness level &lt;br/&gt;<br /> - Your work focus &lt;br/&gt;<br /> - Your energy level &lt;br/&gt;<br /> <br /> '''Where I will get the data? '''&lt;br/&gt;<br /> - My roommate is a nutrition specialist and member of huge fitness project so his advice will be the source of my dependencies between values as well as values for different types of nutrition. &lt;br/&gt;<br /> - Nutrition plans.&lt;br/&gt; <br /> <br /> '''How will I get the output?'''&lt;br/&gt;<br /> I would like to use NetLogo to be able to see both values (numbers) and also monthly progress on the display (maybe a characted running between job, eating and gym. Showing HP bar? :D).<br /> If this kind of simulation is not suitable for NetLogo then Vensim? I have no idea how would I transfer the problem into equitations which are required.<br /> <br /> :: You got it right - it is Vensim topic without any doubt. Without mathematical formulation you can not do any simulation, so you have to deal with mathematical model formulation anyway.So, if Vensim is ok with you, than we can approve it. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:20, 23 December 2018 (CET)<br /> :::: Then I will go with Vensim and try to put together something meaningful! [[User:Jan.pippal|Jan Pippal]]<br /> :::::: OK. Then '''approved'''. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 12:29, 24 December 2018 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16812 2018-12-30T12:24:01Z

<p>Xvegm00: </p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> <br /> <br /> <br /> ----<br /> <br /> == Simulation proposal ([[Xvegm00|Xvegm00]]) Simulation of semi-intelligent algae (Not approved yet)==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> ''Assignment''<br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 13:23, 30 December 2018 (CET)<br /> <br /> == Simulation proposal ([[xkorj58|xkorj58]]) Find the thief (Not approved yet)==<br /> <br /> This simulation should be created as a space search. There shall be two types of agents - first ones, police officers looking for a thief. Second a thief (moveable or imoveable) which is about to be found. Room itself could have obstacles. Police officers will have a vision, can have memory, could be various number of them and level of shared information about searched fields.<br /> <br /> <br /> ''Assignment''<br /> <br /> Title: Find the thief<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Jiří Korčák<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xkorj58|Xkorj58]] ([[User talk:Xkorj58|talk]]) 16:03, 17 December 2018 (CET)<br /> <br /> == Simulation proposal (dolj04) ==<br /> <br /> Topic/goal: '''Optimal size of HDD for virtual Digitization server'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - each day an average of 47 batches of documents is being processed by the server with average size per batch of 32 MB (calculated from customers server)&lt;br/&gt;<br /> - the number of batches changes a lot and cant be easily predicted so it will have to be taken into consideration (from sample: lowest number of batches scanned in a day is 13, the highest is 134)&lt;br/&gt;<br /> - the average size is not changing that much&lt;br/&gt;<br /> - (batch contains original scanned documents, extracted data in XML files, log files, enhanced images and searchable PDF)&lt;br/&gt;<br /> - backup images from scanning will stay on the server for 6 months (those are ''additional'' ~50 % of the batch size)&lt;br/&gt;<br /> - successfully processed batches older than 14 days are deleted every day&lt;br/&gt;<br /> - for precaution lets say around 5% of batches wont be processed correctly&lt;br/&gt;<br /> - those will stay on the server and will be processed every month by admins &lt;br/&gt;<br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you will base the simulation? I do not see any causal loops in the issue you are trying to solve, using Vesim does not make much sense then- this topic suits the Monte Carlo if you have the data to derive the parameters from. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:38, 18 December 2018 (CET)<br /> <br /> :::: I would like to take the data at work as I have access to production server which is used by one of our customers for digitization and I will use Monte Carlo as you suggested.<br /> <br /> ::::::OK.'''Approved'''. Make sure that the derivation of probability distributions out of the real data for generating the random values is also part of your paper.[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:38, 20 December 2018 (CET)<br /> <br /> == Simulation proposal (svem02 [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 18:42, 18 December 2018 (CET)) ==<br /> <br /> Topic: '''likelihood of infection with flu'''<br /> <br /> Definition of the problem: &lt;br/&gt;<br /> - everyone has a certain probability of getting sick with a flu, this model calculates the probability based on the people you are in contact with (two types of people, infected, not infected). Other variables and levels are available (e.g. infestation, total population&lt;br/&gt;<br /> <br /> <br /> Simulation environment: Vensim<br /> <br /> :: on what data you would set up the simulation? how would you simulate the individual people and their connection in Vensim? (in my opinon this topic fits multiagent simulation) [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:30, 18 December 2018 (CET)<br /> <br /> :::: I would base the simulation on how much one person interacts with others, depending on this variable the person would have some probability of getting sick thus the number of ill people would increase thus the probability of him getting sick would be higher and higher. The variable infestation would represent how much the illness is easily transimited to other people. [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 14:04, 19 December 2018 (CET)<br /> :::::: I do not see it as simulation - not much of randomness, no causal feedback loops and pretty obvious result (the longer the simulation would run, the more sick people or that one gets sick) with no practical use. Not to mention, there is no data to derive the equations needed. Either reformulate it for the Netlogo so that it has some useful results based on some real data, or try something else. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 14:27, 19 December 2018 (CET)<br /> :::::::: Ok, I will try something else. New proposal at the bottom of the page [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:46, 19 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal ([[User:xkaij00|xkaij00]] ([[User talk:xkaij00|talk]]) 21:20, 18 December 2018 (CET)) ''(Not approved yet)'' ==<br /> <br /> Topic: '''Social and economical effects of reunification of North &amp; South Korea''' &lt;br/&gt; &lt;br/&gt;<br /> '''Definition of the problem:''' &lt;br/&gt;<br /> Let's hope one day South and North Korea will be reunited. That would mean a big fluctuation of people between the two separated states: &lt;br/&gt;<br /> - North Koreans will migrate to South Korea area, look for flats, try to get jobs and receive welfare. &lt;br/&gt;<br /> - South Koreans will invest in North Korea area and create jobs and factories there. &lt;br/&gt; &lt;br/&gt;<br /> ''I would like to simulate:'' &lt;br/&gt;<br /> - What will be the population ratio of the two areas per square meter. &lt;br/&gt;<br /> - How many North Koreans will move to South Korea area after the reunification and how many of them will be on welfare and how much that will cost the new reunited country. &lt;br/&gt;<br /> - How much will the suicide rate change in Korea after the reunification since it's a known fact that defected North Koreans have a high suicide rate due to the fact they have difficulties to adjust to the new lifestyle and process propaganda-free information. &lt;br/&gt;<br /> - What will be the housing situation after the reunification. &lt;br/&gt;<br /> - How will the GDP of the new country possibly develop. &lt;br/&gt;<br /> - Possibly how much money will be needed from international help. &lt;br/&gt; &lt;br/&gt;<br /> '''Simulation environment:''' NetLogo<br /> <br /> ----<br /> : I must say it is a really interesting topic, but I am afraid it is a bit too ambitious. Please, if you are interested in this topic, elaborate in detail, how you would solve it. What agents would you use, what parameters, how the simulation would look like, on what data it should be based (and where you get them). And perhaps focus on fewer goals. To be honest, I am still not sure if this is doable, because of the need for extensive research. But, try to think about it yourself. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 19:26, 19 December 2018 (CET)<br /> ----<br /> :: Thank you for the feedback - I tried to break down and simplify the simulation:<br /> <br /> :: '''Agents:'''<br /> <br /> :: - North Koreans (0-14 years)<br /> :: - North Koreans (15-24 years)<br /> :: - North Koreans (25-54 years)<br /> :: - North Koreans (55-64 years)<br /> :: - North Koreans (65 years and over)<br /> <br /> :: - South Koreans (0-14 years)<br /> :: - South Koreans (15-24 years)<br /> :: - South Koreans (25-54 years)<br /> :: - South Koreans (55-64 years)<br /> :: - South Koreans (65 years and over)<br /> <br /> :: - South Korea listed companies<br /> :: - Foregin investors<br /> <br /> :: '''Parameters'''<br /> :: - North Korea GDP<br /> :: - North Korea GDP growth<br /> <br /> :: - South Korea GDP<br /> :: - South Korea GDP growth<br /> <br /> :: - North Korea area [sq. meters]<br /> :: - South Korea area [sq. meters] <br /> <br /> :: - North Korea unemployment rate<br /> :: - South Korea unemployment rate<br /> :: - South Korea average salary [[https://tradingeconomics.com/south-korea/wages]]<br /> <br /> :: - North Korea suicide rate ([[https://www.theguardian.com/world/2014/sep/04/north-korea-suicide-rate-among-worst-world-who-report]])<br /> :: - Income / Suicide Rate ratio ([[https://www.businessinsider.com/link-between-wealth-and-suicide-rates-san-francisco-federal-reserve-2012-11]])<br /> <br /> :: - Migration rates from East to West Germany after the fall of the Iron Curtain [[https://www.demographic-research.org/volumes/vol11/7/11-7.pdf]] (will be used as a reference for random migration rates)<br /> <br /> :: '''Targets of the simulation:'''<br /> <br /> :: - Determine population distribution based on migration rates (random based on data from Eastern/Western Germany case) and population<br /> :: - Determine unemployment in the reunited country<br /> :: - Determine suicide rates of North Koreans based on welfare / amount of underage persons (students) / average salary / amount of retired persons<br /> :: - Determine new GDP based on above data (foreign investors being random)<br /> <br /> :: '''Data sources:'''<br /> <br /> :: Apart from the sources linked above, the data on North Korea will be used from CIA website [[https://www.cia.gov/library/publications/the-world-factbook/geos/kn.html]], comparable data on South Korea will be used from the same source for consistence [[https://www.cia.gov/librarY/publications/the-world-factbook/geos/ks.html]] and other specific data on South Korea will be pulled from Wikipedia.<br /> <br /> :: ''The running simulation should show charts of the suicide rates, unemployment and GDP in time and the absolute numbers for the population distribution.''<br /> <br /> :: Please let me know if this narrowing-down and breakdown is sufficient :) Thank you.<br /> <br /> :: [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 00:48, 20 December 2018 (CET)<br /> <br /> ::: The relevance of comparison with Germany is questionable, but, ok. Let's say there perhaps isn't any better comparison. Nevertheless, I still don't understand, how the simulation should work. How the agents will act? How it will be measured? How you will be able to calculate all the figures? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:28, 21 December 2018 (CET)<br /> <br /> :::: OK, I thought about how I would realize the simulation in NetLogo and I see you were right about it being too complicated. So I tried to simplify my model even more to be doable:<br /> :::: - The patches will be divided proportionally by area of the two countries, they will also be characterized by a certain portion of the initial GDP as their wealth.<br /> :::: - The turtles representing Koreans will have initial wealth (also based on the initial GDP) and they will be migrating towards one of the two states based on their inclination to migrate to the other country. This inclination will be calculated randomly based on the East-West Germany migration data and their wealth.<br /> :::: - On each tick, the simulation will determine:<br /> ::::: - if the turtle (Korean) has a job (landed one, got fired, is working, or didn't get hired) based semi-randomly on the wealth of the patch the turtle is on.<br /> ::::: - The wealth of the patch and turtle -&gt; if the turtle is employed, it gains wealth and also generates a little wealth for the patch; If unemployed, it drains the wealth of the patch a little and its wealth decreases a little.<br /> ::::: - Based on the suicide rates, employment and wealth, it will also semi-randomly decide if the person is likely to kill themselves.<br /> :::: That way we can at least simulate and see the wealth distribution, suicide rates and population distribution in a very simplified model... Does that makes sense or am I still in over my head? :) [[User:Xkaij00|Xkaij00]] ([[User talk:Xkaij00|talk]]) 19:20, 21 December 2018 (CET)<br /> <br /> == Simulation proposal (bobj00) ([[User:Bobrekjiri|Bobrekjiri]] ([[User talk:Bobrekjiri|talk]]) 13:49, 19 December 2018 (CET)) ''(Not approved yet)'' ==<br /> <br /> Topic/goal: '''Comparing the efficiency of Diamond interchange and Diverging diamond interchange'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - [https://en.wikipedia.org/wiki/Diverging_diamond_interchange Diverging diamond interchange] is an alternative to Diamond interchange that is being used in France since 1970s and was brought to light recently (2009) in USA because it should be more effective than Diamond interchange in terms of waiting times and also in terms of safety (fewer crossing points of traffic).&lt;br/&gt;<br /> - Goal of the simulation is to measure if the statement about lower waiting times is correct and under which conditions (traffic load, traffic lights setup).&lt;br/&gt;<br /> - Throughput and waiting times will be measured under several traffic conditions, for example: most cars exiting highway are heading south or most cars entering highway are heading west, etc.&lt;br/&gt;<br /> - Model does not include simulation of traffic accidents, so the safety cannot be measured and will not be part of the simulation.&lt;br/&gt;<br /> <br /> '''Simulation environment:''' Netlogo<br /> '''Approved''' [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:29, 21 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 16:52, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Maze runner vs fire'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There will be a maze, in one corner a person, who tries to find the way out, in the other corner will be fire spreading rapidly. Does the person survives or dies by fire? Person and fire wont be able to jump through walls.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : What is the purpose of the simulation? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:30, 21 December 2018 (CET)<br /> :: to find out whether a person who is lost in a bulding will survive. Imagine yourself in a bulding you have never been before and a fire appears. Wouldn’t it be interesting to know If you would find your way out? [[User:Martin svejda|Martin svejda]] ([[User talk:Martin svejda|talk]]) 22:51, 21 December 2018 (CET)<br /> ----<br /> <br /> ==Simulation proposal [[User:qnesa01|qnesa01]] ==<br /> <br /> Topic/goal: '''Traffic simulation Argentinska street'''<br /> <br /> '''Situation:'''&lt;br/&gt;<br /> - Argentinska street is one of the important roads in Prague that connects Bubenské nábřeží with bridge “Barikádníků”. It leads from city centre and other parts of city outside to the northern part of the country, and to Germany or Poland. Moreover, Argentinska street connects hospital “Na Bulovce” with city centre and other parts. For emergency vehicles it is the way how to get faster, where help is needed. <br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - There is traffic on the road during mornings and evenings, which makes people wait sometimes hours to get out and more important it makes difficult pass for emergency vehicles. <br /> <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> In the scope is part of Argentinska street from Bubenské nábřeží till Dělnická street plus street Za Viaduktem, part of Jateční and part of Tusarova street as they have influence on the whole situation of Argentinska traffic. The purpose of the simulation is to find ways how to make traffic less. In order to do it will be checked, first, if it is possible to change lights more efficiently for cars flow. Second, answer the question – if we can add only one line only to one direction, which direction we have to choose: to city centre or from city centre? <br /> <br /> <br /> '''Simulation environment''': Simprocess; SUMO (for traffic representation) <br /> <br /> '''Brief process of simulation :'''&lt;br/&gt;<br /> 1) Data collection. Data will be collected manually (observation) and from HERE traffic API. Manually for morning, midday and evening during 30 mins each part of the day within one week. At the end of data collecting the average distribution will be made based on the data. <br /> 2) Real situation simulation <br /> 3) Based on simulation of real situation will be checked the efficiency of lights changes <br /> 4) The hypothetical model of adding one more line will be created based on simulation of real situation <br /> 5) Summary<br /> <br /> : Ok, SUMO could be used for this. I just don't understand what you will simulate in SUMO, and what you will simulate in Simprocess, and why? The combination of two tools is uneasy, so there should be a good reason. However sounds interesting. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:41, 21 December 2018 (CET)<br /> : The Simprocess will be used for problem solution and SUMO for traffic representation of ready solution. Because SUMO does not have strong performance in searching solution, but very good for clear representation of ready result. Combination of two tools will be useful for this case. [[User:qnesa01|qnesa01]] ([[User talk:qnesa01|talk]]) 21:47, 27 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) (Jindřich Kadoun) 18:52, 19 December 2018 (CET) ==<br /> <br /> <br /> <br /> Topic/goal: '''Survival of the apocalypse'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> - This simulation would aim to showcase in what conditions can humanity survive in the zombie-apocalyptic scenario. &lt;br/&gt;<br /> <br /> The usual scenario in which zombie appears is very simple: there are some number of patiants zero and the rules<br /> for spreading the plague is to get bitten by the infected. There is also a percantual chance of beeing immune against<br /> the plague. Zombies are usually slower than human beeings and behave on the basic of their nearest vision. <br /> <br /> Numerous factors can be acounted for survival, like ability for humanity to reproduce, their number, immunity,<br /> effectivnes of the plague.<br /> <br /> '''Simulation environment''': Netlogo<br /> <br /> : Please, no zombie-topics! The problem is that you cannot verify such an assignment with real data. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:43, 21 December 2018 (CET)<br /> <br /> :: Alright then. My second suggestion would be simulating slime mold searching for food. (gif representing the mold [[https://www.reddit.com/r/gifs/comments/a1geie/slime_mold_searching_for_food/|here]] ) I should be able to confront that with a reality and the number of “foods” and speed of the mold could be variable. [[User:Kadj02|Kadj02]] ([[User talk:Kadj02|talk]]) 23:36, 21 December 2018 (CET)<br /> ----<br /> <br /> == Simulation proposal [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 22:33, 19 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Medieval Battle Simulation'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> Two kingdoms come to a dispute and after extensive diplomacy had failed, they take up arms and go to battle.&lt;br/&gt;<br /> Each kingdom can have different number of units, but they each choose from the same kind of units (unit types are better against some and weaker to other).&lt;br/&gt;<br /> Each side has their own staging area, but within that area the units can spawn at random locations to test different strategic formations.&lt;br/&gt;<br /> When they meet in battle, they fight to the last man who wins the dispute for his king.&lt;/br&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> It simulates medieval combat on battlefield between two sides.&lt;br/&gt;<br /> The user can select which type of units and how many will each kingdom have.&lt;br/&gt;<br /> Repeated simulation can lead to conclusions on what strategy the kings should focus on and which units they should train for successful reign when facing a violent foe.<br /> <br /> '''Simulation environment:''' NetLogo<br /> <br /> : I would narrow this down to testing battle strategies. Forget kingdoms. You can perform a research on some historical strategies, their pros and cons, choose some, and try to simulate them, and compare. If it sounds meaningful, please, redefine the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:48, 21 December 2018 (CET)<br /> <br /> :: The part about kingdoms was more of a story telling. The objects in NetLogo would just be the units (of different types). I envisioned them to spawn at random on their given side of the plane, but if it's necessary I could include spawning in historical formations. Would that be ok? [[User:Xlazl00|Xlazl00]] ([[User talk:Xlazl00|talk]]) 11:00, 22 December 2018 (CET)<br /> <br /> ----<br /> <br /> == Simulation proposal [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 09:30, 20 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Proof of a business plan - simulation of capacities of a Café'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> At the moment we have a venue in a small town under construction which we would like to turn into a café.&lt;br/&gt; <br /> The spatial dispositions are set and now there are many questions like: how to set up tables, find out how many people can be at one time in the café, if the café is profitable when the amount of people coming in is low/medium/high, etc. &lt;br/&gt;<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> As said, I would like to simulate a process of people coming in to find out the right amount of seats and tables when knowing there will be just one staff member at the time.&lt;br/&gt; <br /> I would also like to find out whether it is even possible to manage the whole place being just one person responsible for everything and/or whether it is cost efficient.&lt;br/&gt;<br /> &lt;br/&gt;<br /> The output data should help us find out whether the whole concept is viable and based on the findings we could adjust the business plan.<br /> <br /> '''Simulation environment:''' I would like to use Simprocess for the simulation of the venue setting and probably complement it with some calculations in Excel.<br /> <br /> : I like the idea, just hesitate about the tool. As far as I understand, a substantial part of the whole thing is about the spatial arrangement of the café. Then Simprocess is not the first choice. Why do you want to use it? [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 18:56, 21 December 2018 (CET)<br /> <br /> ::I liked that Simprocess allowed me to work with resources. And I see the tables and chairs as a resource, as well as the working power. If the flow of people coming in is well set up, I thought, by adjusting the resources, it should be possible to find out the optional setting. Do you propose any other tool to do that? I will be greatful for other ideas to improve the solution as it is a real problem we would like to solve. [[User:Xsmyt00|Xsmyt00]] ([[User talk:Xsmyt00|talk]]) 10:21, 27 December 2018 (CET)<br /> <br /> == Simulation proposal [[User:Manj01|Manj01]] ([[User talk:Manj01|talk]]) 15:21, 21 December 2018 (CET) ==<br /> <br /> Topic/goal: '''Effective class configuration a plane between Prague and Dubai'''<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> <br /> There is a new airline operating between Prague and Dubai. The want to configure their planes as effectively as possible. They have one second hand Boeing 777 (396 seats in economy class only configuration)<br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Company wants to operate daily air route between Prague and Dubai. The company can have up to four classes - economy, economy plus, business and first. Seat in higher class takes more space, but generates more money. Prices are available from companies currently operating on this route. Demand predictions can be made based on class configurations used by companies already operating on this and similar routes (6-hours mainly holiday) destinations.<br /> <br /> '''Simulation environment:''' I would like to use MS Excel to do Monte Carlo simulation.<br /> <br /> :: I just wonder, where is the randomnes, as you want to just derive the configuration from the already operating competition. If it should be a simulation, you would have to have real daily data (for reasonable long period, a year minimum) for the demand for the flights (there and back) and also for the individual classes. Only then you are able to derive probability distributions for the simulation that would make sense. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 22:31, 21 December 2018 (CET)<br /> <br /> <br /> == Simulation proposal (xpipj04) ==<br /> <br /> Topic/goal: You are what you eat<br /> <br /> '''Definition of the problem:'''&lt;br/&gt;<br /> Most people nowadays want to hit the gym or eat healthy to get fit and at the same time take a long high quality sleep and work effectively. All the success in life and your energy comes from the food (nutrition values) you consume a day and the regime you have. The new year is knocking the door and you want to make sure that you will plan your food and regime effectively so your life is in balance. <br /> <br /> '''Purpose of simulation:'''&lt;br/&gt;<br /> Based on the input you choose the simulation should be able to give you the expected levels of achieved energy, work efficiency and especially if you are about to get fit or you will drop down to be a couch potato. <br /> <br /> '''Input parameters'''&lt;br/&gt;<br /> - Food consumed - listed types of food with calculated nutrition values (protein, fat, sugar, energy) &lt;br/&gt;<br /> - Time spent at gym (kardio, workout, culturist) &lt;br/&gt;<br /> - Time spent at work - type of work (manual, manager) &lt;br/&gt;<br /> - Time spent sleeping &lt;br/&gt;<br /> <br /> '''Output'''&lt;br/&gt;<br /> - Your BMI &lt;br/&gt;<br /> - Your fitness level &lt;br/&gt;<br /> - Your work focus &lt;br/&gt;<br /> - Your energy level &lt;br/&gt;<br /> <br /> '''Where I will get the data? '''&lt;br/&gt;<br /> - My roommate is a nutrition specialist and member of huge fitness project so his advice will be the source of my dependencies between values as well as values for different types of nutrition. &lt;br/&gt;<br /> - Nutrition plans.&lt;br/&gt; <br /> <br /> '''How will I get the output?'''&lt;br/&gt;<br /> I would like to use NetLogo to be able to see both values (numbers) and also monthly progress on the display (maybe a characted running between job, eating and gym. Showing HP bar? :D).<br /> If this kind of simulation is not suitable for NetLogo then Vensim? I have no idea how would I transfer the problem into equitations which are required.<br /> <br /> :: You got it right - it is Vensim topic without any doubt. Without mathematical formulation you can not do any simulation, so you have to deal with mathematical model formulation anyway.So, if Vensim is ok with you, than we can approve it. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 08:20, 23 December 2018 (CET)<br /> :::: Then I will go with Vensim and try to put together something meaningful! [[User:Jan.pippal|Jan Pippal]]<br /> :::::: OK. Then '''approved'''. [[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 12:29, 24 December 2018 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Xvegm00&diff=16657 2018-12-15T10:10:20Z

<p>Xvegm00: </p> <hr /> <div><br /> == Simulation of semi-intelligent algae ==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> <br /> A team of Japanese and Hungarian researchers have shown P. polycephalum can solve the Shortest path problem. When grown in a maze with oatmeal at two spots, P. polycephalum retracts from everywhere in the maze, except the shortest route connecting the two food sources. When presented with more than two food sources, P. polycephalum apparently solves a more complicated transportation problem. With more than two sources, the amoeba also produces efficient networks. In a 2010 paper, oatflakes were dispersed to represent Tokyo and 36 surrounding towns. P. polycephalum created a network similar to the existing train system, and &quot;with comparable efficiency, fault tolerance, and cost&quot;. <br /> <br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> <br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> == Assignment ==<br /> <br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo<br /> <br /> [[User:Xvegm00|Xvegm00]] ([[User talk:Xvegm00|talk]]) 11:10, 15 December 2018 (CET)</div>

Xvegm00 http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16656 2018-12-15T10:08:58Z

<p>Xvegm00: </p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> [[Xvegm00|Xvegm00]]</div>

Xvegm00 http://www.simulace.info/index.php?title=Xvegm00&diff=16655 2018-12-15T10:08:54Z

<p>Xvegm00: Created page with &quot; == Simulation of semi-intelligent algae == This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a si...&quot;</p> <hr /> <div><br /> == Simulation of semi-intelligent algae ==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> <br /> A team of Japanese and Hungarian researchers have shown P. polycephalum can solve the Shortest path problem. When grown in a maze with oatmeal at two spots, P. polycephalum retracts from everywhere in the maze, except the shortest route connecting the two food sources. When presented with more than two food sources, P. polycephalum apparently solves a more complicated transportation problem. With more than two sources, the amoeba also produces efficient networks. In a 2010 paper, oatflakes were dispersed to represent Tokyo and 36 surrounding towns. P. polycephalum created a network similar to the existing train system, and &quot;with comparable efficiency, fault tolerance, and cost&quot;. <br /> <br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> <br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> == Assignment ==<br /> <br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo</div>

Xvegm00 http://www.simulace.info/index.php?title=Assignments_WS_2018/2019&diff=16654 2018-12-15T10:04:27Z

<p>Xvegm00: </p> <hr /> <div>{{Ambox<br /> | text = &lt;div&gt;<br /> Please, put here your assignments. Do not forget to sign them. You can use &lt;nowiki&gt;~~~~&lt;/nowiki&gt; (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment|How to deal with the simulation assignment]].<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | text = &lt;div&gt;<br /> Topics on gambling, cards, etc. are not welcome.<br /> &lt;/div&gt;<br /> }}<br /> <br /> {{Ambox<br /> | type = content<br /> | text = &lt;div&gt;<br /> In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br /> &lt;/div&gt;<br /> }}<br /> <br /> [[[http://www.simulace.info/index.php/User:Xvegm00]]]</div>

Xvegm00 http://www.simulace.info/index.php?title=User:Xvegm00&diff=16653 2018-12-15T10:02:21Z

<p>Xvegm00: </p> <hr /> <div><br /> == Simulation of semi-intelligent algae ==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> <br /> A team of Japanese and Hungarian researchers have shown P. polycephalum can solve the Shortest path problem. When grown in a maze with oatmeal at two spots, P. polycephalum retracts from everywhere in the maze, except the shortest route connecting the two food sources. When presented with more than two food sources, P. polycephalum apparently solves a more complicated transportation problem. With more than two sources, the amoeba also produces efficient networks. In a 2010 paper, oatflakes were dispersed to represent Tokyo and 36 surrounding towns. P. polycephalum created a network similar to the existing train system, and &quot;with comparable efficiency, fault tolerance, and cost&quot;. <br /> <br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> <br /> Video about physarum: [https://www.youtube.com/embed/HyzT5b0tNtk]<br /> <br /> == Assignment ==<br /> <br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo</div>

Xvegm00 http://www.simulace.info/index.php?title=User:Xvegm00&diff=16652 2018-12-15T10:00:54Z

<p>Xvegm00: </p> <hr /> <div><br /> == Simulation of semi-intelligent algae ==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> <br /> A team of Japanese and Hungarian researchers have shown P. polycephalum can solve the Shortest path problem. When grown in a maze with oatmeal at two spots, P. polycephalum retracts from everywhere in the maze, except the shortest route connecting the two food sources. When presented with more than two food sources, P. polycephalum apparently solves a more complicated transportation problem. With more than two sources, the amoeba also produces efficient networks. In a 2010 paper, oatflakes were dispersed to represent Tokyo and 36 surrounding towns. P. polycephalum created a network similar to the existing train system, and &quot;with comparable efficiency, fault tolerance, and cost&quot;. <br /> <br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> <br /> [&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/HyzT5b0tNtk&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;]<br /> <br /> == Assignment ==<br /> <br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo</div>

Xvegm00 http://www.simulace.info/index.php?title=User:Xvegm00&diff=16651 2018-12-15T10:00:32Z

<p>Xvegm00: </p> <hr /> <div><br /> == Simulation of semi-intelligent algae ==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> <br /> A team of Japanese and Hungarian researchers have shown P. polycephalum can solve the Shortest path problem. When grown in a maze with oatmeal at two spots, P. polycephalum retracts from everywhere in the maze, except the shortest route connecting the two food sources. When presented with more than two food sources, P. polycephalum apparently solves a more complicated transportation problem. With more than two sources, the amoeba also produces efficient networks. In a 2010 paper, oatflakes were dispersed to represent Tokyo and 36 surrounding towns. P. polycephalum created a network similar to the existing train system, and &quot;with comparable efficiency, fault tolerance, and cost&quot;. <br /> <br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> <br /> &lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/HyzT5b0tNtk&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;<br /> <br /> == Assignment ==<br /> <br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo</div>

Xvegm00 http://www.simulace.info/index.php?title=User:Xvegm00&diff=16650 2018-12-15T10:00:02Z

<p>Xvegm00: Created page with &quot; == Simulation of semi-intelligent algae == This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a si...&quot;</p> <hr /> <div><br /> == Simulation of semi-intelligent algae ==<br /> <br /> This Netlogo simulation aims to copy the behaviour of a symbiotic organism called physarum polycephalum. Physarum is actually a single cell organism, but when two or more cells meet, their membranes merge together and they work together to efficiently gather nutrition and multiply.<br /> <br /> A team of Japanese and Hungarian researchers have shown P. polycephalum can solve the Shortest path problem. When grown in a maze with oatmeal at two spots, P. polycephalum retracts from everywhere in the maze, except the shortest route connecting the two food sources. When presented with more than two food sources, P. polycephalum apparently solves a more complicated transportation problem. With more than two sources, the amoeba also produces efficient networks. In a 2010 paper, oatflakes were dispersed to represent Tokyo and 36 surrounding towns. P. polycephalum created a network similar to the existing train system, and &quot;with comparable efficiency, fault tolerance, and cost&quot;. <br /> <br /> This simulation should mimic the spread and path creation of the algae, as well as its ability to solve the shortest path problem.<br /> <br /> [[Media:&lt;iframe width=&quot;560&quot; height=&quot;315&quot; src=&quot;https://www.youtube.com/embed/HyzT5b0tNtk&quot; frameborder=&quot;0&quot; allow=&quot;accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture&quot; allowfullscreen&gt;&lt;/iframe&gt;]]<br /> <br /> == Assignment ==<br /> <br /> Title: Simulation of semi-intelligent algae<br /> <br /> Course: 4IT496 Simulace systémů (v angličtině) (WS 2018/2019)<br /> <br /> Author: Bc. Martin Vegner<br /> <br /> Model type: Multiagent<br /> <br /> Modeling tool: NetLogo</div>

Xvegm00