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 behaviour. 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 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.
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