—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)
This work investigates some uses of self-monitoring in classiier systems (CS) using Wilson's recent XCS system as a framework. XCS is a signiicant advance in classiier systems technology which shifts the basis of tness evaluation for the Genetic Algorithm (GA) from the strength of payoo prediction to the accuracy of payoo prediction. Initial work consisted… (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)
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)
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)
[Learning] Classifier systems are a kind of rule-based system with general mechanisms for processing rules in parallel, for adaptive generation of new rules, and for testing the effectiveness of existing rules. These mechanisms make possible performance and learning without the " brittleness " characteristic of most expert systems in AI.
We analyse the concept of strong overgeneral rules, the Achilles' heel of traditional Michigan-style learning classifier systems, using both the traditional strength-based and newer accuracy-based approaches to rule fitness. We argue that different definitions of overgenerality are needed to match the goals of the two approaches, present minimal conditions… (More)