A Mathematical Framework for Studying Learning in Classifier Systems

Abstract

Massively parallel, rule-based systems offer both a practical and a theoretical tool for understanding systems that act usefully in complex environments [see, for example, refs 1-4], However, these systems pose a number of problems of a high order of difficulty - problems that can be broadly characterized as problems in nonlinear dynamics. The difficulties stem from the fact that the systems are designed to act in environments with complex transition functions - environments that, in all circumstances of interest, are far from equilibrium. Interactions with the environment thus face the systems with perpetual novelty, and the usual simplifications involving fixed points, limit cycles, etc., just do not apply.