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Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment. The term is also sometimes used in ethology or animal behavior. One problem for understanding action selection is determining the level of abstraction used for specifying an "act". At the most basic level of abstraction, an atomic act could be anything from ''contracting a muscle cell'' to ''provoking a war''. Typically for any one action-selection mechanism, the set of possible actions is predefined and fixed. Most researchers working in this field place high demands on their agents: * The acting agent typically must select its action in dynamic and unpredictable environments. * The agents typically act in real time; therefore they must make decisions in a timely fashion. * The agents are normally created to perform several different tasks. These tasks may conflict for resource allocation (e.g. can the agent put out a fire and deliver a cup of coffee at the same time?) * The environment the agents operate in may include humans, who may make things more difficult for the agent (either intentionally or by attempting to assist.) * The agents themselves are often intended to model animals or humans, and animal/human behaviour is quite complicated. For these reasons action selection is not trivial and attracts a good deal of research. == Characteristics of the action selection problem == The main problem for action selection is complexity. Since all computation takes both time and space (in memory), agents cannot possibly consider every option available to them at every instant in time. Consequently, they must be biased, and constrain their search in some way. For AI, the question of action selection is ''what is the best way to constrain this search''? For biology and ethology, the question is ''how do various types of animals constrain their search? Do all animals use the same approaches? Why do they use the ones they do?'' One fundamental question about action selection is whether it is really a problem at all for an agent, or whether it is just a description of an emergent property of an intelligent agent's behavior. However, if we consider how we are going to build an intelligent agent, then it becomes apparent there must be ''some'' mechanism for action selection. This mechanism may be highly distributed (as in the case of distributed organisms such as social insect colonies or slime mold) or it may be a special-purpose module. The action selection mechanism (ASM) determines not only the agent’s actions in terms of impact on the world, but also directs its perceptual attention, and updates its memory. These egocentric sorts of actions may in turn result in modifying the agents basic behavioural capacities, particularly in that updating memory implies some form of machine learning is possible. Ideally, action selection itself should also be able to learn and adapt, but there are many problems of combinatorial complexity and computational tractability that may require restricting the search space for learning. In AI, an ASM is also sometimes either referred to as an agent architecture or thought of as a substantial part of one. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Action selection」の詳細全文を読む スポンサード リンク
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