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Predicting the size of the antibody combining region from consideration of efficient self/non-self discrimination. Proceedings of the National Academy of Science, 90:1691–1695, 1993. 59. Rick L. Riolo. Lookahead Planning and Latent Learning in a Classifier System. pages 316–326. A Bradford Book. MIT Press, 1990. 60. Rick L. Riolo. Lookahead planning and latent learning in a classifier system. Ann Arbor, MI, 1991. In the Proceedings of the Simulation of Adaptive Behavior Conference, MIT Press, 1991.

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.

Pier Luca Lanzi. A Study of the Generalization Capabilities of XCS. In B¨ ack [3], pages 418–425. gz. 46. Pier Luca Lanzi. Adding Memory to XCS. In Proceedings of the IEEE Conference on Evolutionary Computation (ICEC98). IEEE Press, 1998. gz. H. Holland et al. 47. Pier Luca Lanzi. Reinforcement Learning by Learning Classifier Systems. PhD thesis, Politecnico di Milano, 1998. 48. Pier Luca Lanzi. An Analysis of Generalization in the XCS Classifier System. Evolutionary Computation, 7(2):125–149, 1999.

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