Reinforcement learning with classifier systems

Consideration is given to the learning classifier system (LCS) as an approach to reinforcement learning problems. An LCS is a type of adaptive expert system that uses a knowledge base of production rules in a low-level syntax that can be manipulated by a genetic algorithm (GA). GAs are a class of computerized search procedures that are based on the mechanics of natural genetics. An important feature of the LCS paradigm is the possible adaptive formation of default hierarchies (layered sets of default and exception rules). An examination is made of the problem of default hierarchy formation under the conventional bid competition method of LCS conflict resolution and the necessity auction and a separate priority factor are suggested as modifications to this method. Simulations show the utility of this method.<
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