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By Pier Luca Lanzi

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E. X × A ⇒ P maps where X is the set of states, A is the set of actions and P is the set of expected payoffs. “It does not learn what input sensation will follow a given action. That is, it does not learn an X × A ⇒ Y map, where Y is the following sensation” (Wilson [83, p. 173]). Or in other words it does not learn an internal world model. Holland [32], Riolo [59], and Stolzmann [72, this volume] show how internal world models can be learned in LCS. g. Sutton [73]; Sutton & Barto [74, p. 233]) Future work will have to show how LCS can be used to learn internal world models with a minimum number of classifiers and how these internal world models can be used in reinforcement learning.

H. Holland et al. 7. John Tyler Bonner. The Evolution of Complexity. Princeton University Press, Princeton, New Jersey, 1988. 8. Lashon B. Booker. Intelligent Behavior as an Adaptation to the Task Environment. PhD thesis, The University of Michigan, 1982. 9. Lashon B. Booker. Do We Really Need to Estimate Rule Utilities in Classifier Systems? In Lanzi et al. [50], pages 125–142. (this volume). 10. Lashon B. Booker, David E. Goldberg, and John H. Holland. Classifier Systems and Genetic Algorithms.

In these two decades they have received increasing attention by many researchers belonging to many areas. Ten years ago Wilson and Goldberg [165] presented a critical review on the first decade of learning classifier systems research discussing the results reached until 1989 and the “future” research directions. , autonomous robotics [36], medical data analysis [8, 65], and personal software agents [166]. Today with this paper we try to provide a roadmap to the second decade of learning classifier system research covering the years from 1989 to 1999.

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