Difference between revisions of "Agent Environments"

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=== Static vs. Dynamic ===
 
=== Static vs. Dynamic ===
– Is the agent the only entity that changes the
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environment in the moment? Is it changing
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In static environments the agent is the only entity there that can actualy change the environment. Again in real world this doesn't happen very often. Usually possibilities of environment changes could be endless. The example of static enviroment could be games or puzzles like hanoi tower or chess. Example of dynamic enviroment is traffic on the road.
during the action of the agent?
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– Chess vs. Traffic
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'''The question:''' Are there other entities that can change the environment or is the agent the only one? Is it changing during an action of the agent?
Environment
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– Many real systems are dynamic, because many entities commonly
 
act concurrently. In fact, there is no clear distinction between these
 
two options. Suppose multitasking on a mono core computer.
 
 
=== Discrete vs. Continuous ===
 
=== Discrete vs. Continuous ===
 
– Is a number of possible actions in the environment finite or infinite?
 
– Is a number of possible actions in the environment finite or infinite?

Revision as of 13:02, 22 January 2018

The Paper topic: Agent Environments

Class: 4IT496 System Simulation (WS 2017/2018)

Author: Bc. David Feldstein

!!!!THIS PAPER IS NOT READY YET!!!!

In multiagent systems there are agents as programmed operating units in certain types of enviroments. The basic distribution of agents is reactive and delibarative agents. Reactive agents exist in the enviroment, they are influenced by its properties and changes however they do not create symbolic representation of the enviroment. Simply they don't try to simulate inteligent decisions or brain work. They don't read the environment, make no logic assumptions, they just react to it. In this case the inteligent behaviour comes from emergencies in the system which will be looked upon closely in below. On the other hand deliberative agents do try to simulate inteligence as we percieve it in our brains. They gather information from the environment and they do create symbolic representation of it. And based on their experience they try to make an adequate inteligent decisions.

This textbook chapter shows differences between different types of agents environments. It offers possible perceptions of the environment and how it affects each individual agent interacting with it and each other.

Environments

There are serveral different points of view in the environments which tells us how the environment can be percieved and helps us adapt the agents to operate in it.

Accessible vs. Inaccessible

In full accessible environment the agent is certain that he can easily get the full information needed any time. This situation typicaly happens in software environment. For example a robot indexing webpages can certainly download all the information that common visiter can see. It is importatnt that the environment must be accessible to the agent in the moment. Theoretic accessibility is not enough.

The question: Can I access complete informations about the environment?

Deterministic vs. Non-deterministic

Deterministic environment tells us that we can rely on certain outcome if we perform an action. For example in calculater I can multiply numbers and I am certain I get the right number. However when lets say automatic reaper cuts grass, can I be sure it is always grass that I cut? Most of agents interacting with real world operate in non-deterministic environments.

The question: Does a action in the environment have a specific effect? Are we certain about the state of environment afterwards?

Static vs. Dynamic

In static environments the agent is the only entity there that can actualy change the environment. Again in real world this doesn't happen very often. Usually possibilities of environment changes could be endless. The example of static enviroment could be games or puzzles like hanoi tower or chess. Example of dynamic enviroment is traffic on the road.

The question: Are there other entities that can change the environment or is the agent the only one? Is it changing during an action of the agent?

Discrete vs. Continuous

– Is a number of possible actions in the environment finite or infinite? – Roulette vs. Legal system – Discreetness and continuity are often a matter of time. If a system is turn-based, it is discrete and often static as well. However, it could be dynamic when more participants make decisions together. 10

Episodic vs. Non-episodic

– Agents operate in certain segments (episodes) that are independent of each other and agent’s state in one episode has no impact on its state in another one. – Discrete simulation in Simprocess vs. whole economy – Distinction between episodic and non-episodic often depends on the viewpoint of the external observer.

Dimensional vs. Dimensionless

– Do spatial characteristics of the environment matter? – Real world vs. internet – If space, dimensions, distances, etc. play a role for agent’s decisions in the environment, then the environment is dimensional. If no of these characteristics are relevant, the environment is dimensionless.

Interpretation of the the environment

states

structured data for SW environments

Input function - can be from simple to very complex task (temperature - machine vision)

transduction problem

Interactions

Environments recognition problem

Often, we cannot simply denote the environment either static or dynamic, either accessible or inaccessible, etc. The environment could have a certain level of the particular trait.

Environment complexity problem

The more inaccessible, non-deterministic, dynamic and continuous the environment, the more complex and less recognizable it is.

The more complex the environment, the more difficult it is to design an agent that should work there.

Time dimension of the environment

The agent is often constrained by time. Is cannot explore and analyze the situation for years, but it has to deliver results in a reasonable time.

short-term X long-term problem (especialy in dynamic environmet)

Subsumption architecture

Reactive agents architecture developed by Brooks. Two key ideas: – Situatedness and embodiment. The agents are physically present

in the environment, draw all their information from the interaction with it and directly influence environment’s dynamics.

– Intelligence and emergence. The intelligence does not exist per se. It emerges from agents’ interactions with the environment and it is not present in single specific component of the system

Agents sense the environment and their percepts directly trigger the proper actions. They are typically as simple as: situation → action.

• Situations are arranged into layers. The lower layer, the more specific behavior and the higher priority.

• The actions are fired concurrently, each layer has its own sensors and effectors.