Abstract
The application of the XCS family of classifier systems within multi-step problems has shown promise, but XCS classifier systems usually solve those problems with a large number of classifiers. If they can generate compact and readily interpretable solutions when used in multi-step problems, it will enhance their applicability and usefulness in a wide range of robot control and knowledge discovery tasks. In this paper, we describe some methods developed for XCS to compact the rule set and acquire explicit policy knowledge for multi-step problems, especially non-Markov problems. Experimental results show the ability to obtain a very compact rule set out of the final population of classifiers with little or no degradation of overall performance.
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