The classifier system framework is a general-purpose approach to learning and representation designed to exhibit non-brittle behavior in complex, continually varying environments.
A New Approach to Encoding Actions in Classifier Systems
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The classifier system framework is a general-purpose approach to learning and representation designed to exhibit non-brittle behavior in complex, continually varying environments. Broadly speaking, classifier systems are expected to avoid brittle behavior because they implement processes that build and refine models of the environment. One of the most important of these processes is categorization. As Holland [5] has pointed out (p. 598) "Categorization is the system's major weapon for combating the environment's perpetual novelty. The system must readily generate categories for input messages, and it must be able to generate categories relevant to its internal processes". Research in classifier systems has focused almost exclusively on finding generalizations for input messages. However, generalizations of actions will also be required in order to build effective models of the environment. This paper introduces a new encoding for actions in classifier rules that lends itself to representing abstract actions.