Reflexive Learning

It is generally accepted by the artificial intelligence community that learning is a desirable and useful capability of a generally intelligent agent. This learning can take a number of forms, and the matter of which type of learning is most appropriate depends both on the researcher and the particular agent in question.

Reflexive learning is learning that is done "automatically", i.e. the agent does not consider the possible costs of learning a particular piece of knowledge. These costs hinge on the usefulness of knowledge: reflexive systems learn everything, even knowledge that does not promise to enhance the agent's behavior. This 'extra' knowledge threatens to slow the agent, since it must be searched each time the agent attempts to retrieve a piece of knowledge. Reflexive architectures try to compensate for this by employing a very efficient matching function, so that extraneous knowledge does not appreciably degrade performance.

It is argued that the reflexive model of learning is more psychologically valid than the deliberative model: humans, in general, cannot help but learn from their experiences, and we certainly are not able to explicitly 'unlearn' something if we decide that it is not worth retaining.

Architectures having this agent property include:

Go to the List of Common Agent Properties.

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