Agents - Revision history

Hram00: /* Agents definition */

2018-01-24T22:25:51Z

‎Agents definition

Hram00: /* Deliberative agents */

2018-01-24T22:23:27Z

‎Deliberative agents

Hram00: /* Hybrid agents */

2018-01-24T22:23:02Z

‎Hybrid agents

Hram00: /* Reactive agents */

2018-01-24T22:21:51Z

‎Reactive agents

Hram00: /* Categorization of agents */

2018-01-24T22:21:01Z

‎Categorization of agents

Hram00: /* Hybrid agents */

2018-01-24T22:19:42Z

‎Hybrid agents

Hram00: /* Agents definition */

2018-01-24T22:18:15Z

‎Agents definition

Hram00: /* Agents definition */

2018-01-24T22:17:59Z

‎Agents definition

Hram00: /* Agents definition */

2018-01-24T22:17:27Z

‎Agents definition

Hram00: /* Agents definition */

2018-01-24T22:16:53Z

‎Agents definition

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 Very similar specific definition is almost twenty years old and comes from G. Weiss (1999)
-'''''“An agent is a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives”'''''
+'''''“An agent is a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives”''''' <ref name="kniha"/>
 From those three definitions we can see that an agent can be almost anything but in many cases, it is more specified and only a certain kind of agents is acceptable for a certain problem.

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 '''Logic-based approach:''' coming from artificial intelligence, agent make logical deduction over a set of known statements. This approach has to deal several challenges. One of them is how to translate the perceptions from the environment into logical statements. There are also risks of logical deadlocks and as well as for the previous approach the calculations can be very demanding for computing power.
-'''BDI approach:''' kind of extended logic-based approach, where BDI stands for belief-desire-intention. It is very sophisticated approach similar to real human reasoning. Belief is a set of knowledge, information about the environment, itself and other agents. Desires represents what the agent want to do, and intentions comes from desires but respecting feasibility. The intentions are typically realised through plans and beliefs are continuously updated.
+'''BDI approach:''' kind of extended logic-based approach, where BDI stands for belief-desire-intention. It is very sophisticated approach similar to real human reasoning. Belief is a set of knowledge, information about the environment, itself and other agents. Desires represents what the agent want to do, and intentions comes from desires but respecting feasibility. The intentions are typically realised through plans and beliefs are continuously updated.<ref name="kniha"/>
 == Hybrid agents ==

← Older revision		Revision as of 22:23, 24 January 2018
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	As previously mentioned agents have their pros and cons, a possible solution to reach the best result is to combine them. The hybrid agent consists of layer, at least one deliberative layer and at least one reactive layer but it can have much more layers. Each layer ha its own behaviour and together with behaviour of other layers creates overall behaviour of the agent.		As previously mentioned agents have their pros and cons, a possible solution to reach the best result is to combine them. The hybrid agent consists of layer, at least one deliberative layer and at least one reactive layer but it can have much more layers. Each layer ha its own behaviour and together with behaviour of other layers creates overall behaviour of the agent.

−	There are tree options of layering: horizontal, vertical with one pass control and vertical with two pass control as shown in the following picture:<ref>ŠALAMON, Tomáš. Design of agent-based models: developing computer simulations for a better understanding of social processes. Řepín-Živonín: Tomáš Bruckner, 2011. ISBN 978-809-0466-111. </ref>	+	There are tree options of layering: horizontal, vertical with one pass control and vertical with two pass control as shown in the following picture:<ref name="kniha">ŠALAMON, Tomáš. Design of agent-based models: developing computer simulations for a better understanding of social processes. Řepín-Živonín: Tomáš Bruckner, 2011. ISBN 978-809-0466-111. </ref>

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 •	'''Situatedness and embodiment:''' It is important for agents to be physically present in the environment and get all the information from sensing the environment or receiving from other agents
-•	'''Intelligence and emergence:''' There is no intelligence of agents themselves, it all comes from their interaction. That is why reactive agents mostly exist as a system, not solely.
+•	'''Intelligence and emergence:''' There is no intelligence of agents themselves, it all comes from their interaction. That is why reactive agents mostly exist as a system, not solely. <ref name="prezentace"/>
 The agents sense, check the condition and perform the action. Each layer has its sensors and effectors and looks following: If condition then action. The lower layer, the higher priority. Let’s look at how Brooks described the layers:

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 '''Natural agents:''' humans, animals, bacteria or viruses can be considered as natural agents. Most often we can come across this term in biology.
-Many authors use distribution of agents based on their way of reasoning for deliberative, reactive and hybrid agents.
+Many authors use distribution of agents based on their way of reasoning for deliberative, reactive and hybrid agents. <ref name="prezentace"/>
 [[File:Agents.png|750x1500px]] <ref name="tech" />

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 Vertical layering with one pass control has a simple interaction between layers but it is not too flexible, and it is vulnerable to failures. It cannot provide feedback between layers.
-Vertical layering with two pass control has less interaction between layers but still very vulnerable to failures and has lower performance <ref>ŠALAMON, Tomáš. Multi-agent systems I. 2011</ref>
+Vertical layering with two pass control has less interaction between layers but still very vulnerable to failures and has lower performance <ref name="prezentace">ŠALAMON, Tomáš. Multi-agent systems I. 2011</ref>
 == Resources ==

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 The second definition specifies the kind of agents we are interested in (it talks about computational agents which belongs to the area of engineering and has nothing to with natural agents from the field of biology such as human). It also says the agent are autonomous, which means they have a control over their actions and operate without direct interventions of other entities. Last difference from previous definition is that they are acting in order to achieve some specific goals.
-[[File:Agent_hram00.png|right|500px]]<ref name="tech"/>
+[[File:Agent_hram00.png|right|500px]]
 An example of an agent according to a previous description is a thermostat. A simple autonomous device which is supposed to maintain temperature in a room. Maintaining the temperature is its goal. It contains a sensor to check what an actual temperature in the room is, a memory to remember what the desired temperature is and the values from the sensor and a decision-making function which compares the measured temperature with the desired one (making this decision is an action) and based on the result (using domain knowledge what to do when actual temperature is lower/higher that desired temperature) commands to turn the heating or the air conditioner on/off (commanding the heating is an effector).

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 Another, more precise definition that extends the one above is from P. Maess:
-'''''“Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are deigned”''''' <ref name="tech">
+'''''“Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are deigned”''''' <ref name="tech"/>
 The second definition specifies the kind of agents we are interested in (it talks about computational agents which belongs to the area of engineering and has nothing to with natural agents from the field of biology such as human). It also says the agent are autonomous, which means they have a control over their actions and operate without direct interventions of other entities. Last difference from previous definition is that they are acting in order to achieve some specific goals.
-[[File:Agent_hram00.png|right|500px]] <ref name="tech">
+[[File:Agent_hram00.png|right|500px]] <ref name="tech"/>
 An example of an agent according to a previous description is a thermostat. A simple autonomous device which is supposed to maintain temperature in a room. Maintaining the temperature is its goal. It contains a sensor to check what an actual temperature in the room is, a memory to remember what the desired temperature is and the values from the sensor and a decision-making function which compares the measured temperature with the desired one (making this decision is an action) and based on the result (using domain knowledge what to do when actual temperature is lower/higher that desired temperature) commands to turn the heating or the air conditioner on/off (commanding the heating is an effector).