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This work investigates some uses of self-monitoring in classi er systems (CS) using Wilson's recent XCS system as a framework. XCS is a signi cant advance in classi er systems technology which shifts the basis of tness evaluation for the Genetic Algorithm (GA) from the strength of payo prediction to the accuracy of payo prediction. Initial work consisted of(More)
This paper extends the work presented in (Kovacs, 1996) on evolving optimal solutions to boolean reinforcement learning problems using Wilson's recent XCS classiier system. XCS forms complete mappings of the payoo environment in the reinforcement learning tradition thanks to its accuracy based tness, which, according to Wilson's Generalization Hypothesis,(More)
In this paper, we take initial steps toward a theory of generalization and learning in the learning classifier system XCS. We start from Wilson’s generalization hypothesis, which states that XCS has an intrinsic tendency to evolve accurate, maximally general classifiers. We analyze the different evolutionary pressures in XCS and derive a simple equation(More)
The problem of how to compromise between speed and accuracy in decision-making faces organisms at many levels of biological complexity. Striking parallels are evident between decision-making in primate brains and collective decision-making in social insect colonies: in both systems, separate populations accumulate evidence for alternative choices; when one(More)
The issue of deletion schemes for classi er systems has received little attention. In a standard genetic algorithm a chromosome can be evaluated (assigned a reasonable tness) immediately. In classi er systems, however, a chromosome can only be fully evaluated after many interactions with the environment, since a chromosome may generalise over many(More)
Many natural and artificial decision-making systems face decision problems where there is an inherent compromise between two or more objectives. One such common compromise is between the speed and accuracy of a decision. The ability to exploit the characteristics of a decision problem in order to vary between the extremes of making maximally rapid, or(More)
Wilson's recent XCS classi er system forms complete mappings of the payo environment in the reinforcement learning tradition thanks to its accuracy based tness. According to Wilson's Generalization Hypothesis, XCS has a tendency towards generalization. With the XCS Optimality Hypothesis, I suggest that XCS systems can evolve optimal populations(More)