New PDF release: Artificial Intelligence - Agents and Environments [math]

By W. Teahan

ISBN-10: 8776815285

ISBN-13: 9788776815288

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Example text

Analogous to the program code starting executing), there is very little control over what happens after that. The washing machine object has methods – for example, fast cycle, spin cycle and so on. It also has state – for example, the time to finish, the temperature and so on. In contrast, human agents are the only agent-oriented solution presently available for this problem. In the future, a domestic robot might perform this task for humans. In this case, from its perspective, it might make the following decisions: “I will now do the washing for you”; “I will fetch the dirty clothes myself”; “I will now recharge myself”.

Attributes Observable and partially observable. Deterministic, stochastic and strategic. Episodic and sequential. Static and dynamic. Discrete and continuous. Single-agent and multiple-agent. Description An agent can be considered to be an agent only if it has the ability to observe its environment (and conversely, the environment itself must therefore be observable). In some cases, usually simple environments, or softwaregenerated environments, all of the environment may be observable. Usually, however, the environment may only be partially observable.

Note that if a deterministic environment is only partially observable to the agent, it will appear to be stochastic from the agent’s point of view. A strategic environment is fully determined by the preceding state combined with the actions of multiple agents. The task environment is episodic if each of the agent’s tasks do not rely on past performance, or cannot affect future performance. If not, then it is sequential. A static environment does not change. In a dynamic environment, if an agent does not respond to the change, this is considered as a choice to do nothing.

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Artificial Intelligence - Agents and Environments [math] by W. Teahan


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