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International Workshop on Learning Classifier Systems

International Workshop on Learning Classifier Systems

IWLCS 2009, IWLCS 2008: Learning Classifier Systems pp 107–126Cite as

Building Accurate Strategies in Non Markovian Environments without Memory

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6471))

Abstract

This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning literature to assess the performances of learning methods when facing problems containing non markovian situations.

Through this study, we discuss on the performance of the APCS upon two mazes (Woods 101 and Maze E2) and also on the efficiency of an improvement of the APCS learning method inspired from the XCS: the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular sub-optimal solution to these maze problems. This statement is shown through a comparison between the results obtained by the XCS on two specific maze problems and those obtained by the APCS.

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Author information

Authors and Affiliations

  1. LAMIA Laboratory, Université des Antilles Guyane, Campus Fouillole, BP 592, 97157, Pointe à Pitre Cedex, Guadeloupe, France

    Énée Gilles & Péroumalnaïk Mathias

Authors
  1. Énée Gilles
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  2. Péroumalnaïk Mathias
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Editor information

Editors and Affiliations

  1. School of Computer Science, ASAP research group, University of Nottingham, Jubilee Campus, Nottingham, NG8 1BB, and Multidisciplinary Centre for Integrative Biology, School of Biosciences, LE12 5RD, Sutton Bonington, UK

    Jaume Bacardit

  2. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, 6140, Wellington, New Zealand

    Will Browne

  3. Department of Brain and Cognitive Sciences, University of Rochester, Meliora Hall, 14627, Rochester, NY, USA

    Jan Drugowitsch

  4. Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins, 2, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  5. Department of Psychology III, University of Würzburg, COBOSLAB – Cognitive Bodyspaces: Learning and Behavior,, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

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Gilles, É., Mathias, P. (2010). Building Accurate Strategies in Non Markovian Environments without Memory. In: Bacardit, J., Browne, W., Drugowitsch, J., Bernadó-Mansilla, E., Butz, M.V. (eds) Learning Classifier Systems. IWLCS IWLCS 2009 2008. Lecture Notes in Computer Science(), vol 6471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17508-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-17508-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17507-7

  • Online ISBN: 978-3-642-17508-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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