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On Lookahead and Latent Learning in Simple LCS

  • Larry Bull
Conference paper
  • 354 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4998)

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.

Keywords

Mutation Rate Classifier System Maze Task Learn Classifier System Latent Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Larry Bull
    • 1
  1. 1.School of Computer ScienceUniversity of the West of EnglandBristolU.K.

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