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On lookahead and latent learning in simple LCS

Bull, Larry

Authors

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor



Abstract

Learning Classifier Systems use evolutionary algorithms to facilitate rule- discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of an accuracy-based scheme where fitness is based on a rule's ability to predict the expected payoff from its use. Learning Classifier Systems that build anticipations of the expected states following their actions are also a focus of current research. This paper presents a simple but effective learning classifier system of this last type, using payoff-based fitness, with the aim of enabling the exploration of their basic principles, i.e., in isolation from the many other mechanisms they usually contain. The system is described and modelled, before being implemented. Comparisons to an equivalent accuracy-based system show similar performance. The use of self-adaptive mutation in such systems in general is then considered. © 2008 Springer Berlin Heidelberg.

Presentation Conference Type Conference Paper (Published)
Publication Date Jan 1, 2008
Publisher Springer Verlag
Volume 4998 LNAI
Pages 154-168
ISBN 9783540881377
DOI https://doi.org/10.1007/978-3-540-88138-4_9
Keywords artificial intelligence, robotics, mathematical logic, formal languages, data mining, knowledge discoverymodels and principles, computation by abstract devices
Public URL https://uwe-repository.worktribe.com/output/1017310
Publisher URL http://dx.doi.org/10.1007/978-3-540-88138-4_9