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Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks

  • Conference paper
Learning Classifier Systems (IWLCS 2006, IWLCS 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4998))

Abstract

There are few contributions to robot autonomous navigation applying Learning Classifier Systems (LCS) to date. The primary objective of this work is to analyse the performance of the strength-based LCS and the accuracy-based LCS, named EXtended Learning Classifier System (XCS), when applied to two distinct robotic tasks. The first task is purely reactive, which means that the action to be performed can rely only on the current status of the sensors. The second one is non-reactive, which means that the robot might use some kind of memory to be able to deal with aliasing states. This work presents a rule evolution analysis, giving examples of evolved populations and their peculiarities for both systems. A review of LCS derivatives in robotics is provided together with a discussion of the main findings and an outline of future investigations.

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Author information

Authors and Affiliations

  1. Laboratory of Bioinformatics and Bio-Inspired Computing - LBiC, School of Electrical and Computer Engineer - FEEC/Unicamp Campinas-SP, Brazil

    Renan C. Moioli & Fernando J. Von Zuben

  2. Centre for Computational Neuroscience and Robotics (CCNR) Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9QH, United Kingdom

    Patricia A. Vargas

Authors
  1. Renan C. Moioli
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  2. Patricia A. Vargas
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  3. Fernando J. Von Zuben
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Editor information

Editors and Affiliations

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

    Jaume Bacardit

  2. Enginyeria i Arquitectura La Salle, Gruß de Recerca en Sistemes Intel.ligents, Quatre Camins 2, Universitat Ramon Llull, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  3. Department of Psychology, University of Würzburg, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

  4. Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Tim Kovacs

  5. Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Lab (IlliGAL), University of Illinois at Urbana-Champaign, 104 S. Mathews Avenue, 61801-2996, Urbana, IL, USA

    Xavier Llorà

  6. Tokyo Institute of Technology, 152-8550, tokyo, Japan

    Keiki Takadama

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© 2008 Springer-Verlag Berlin Heidelberg

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Moioli, R.C., Vargas, P.A., Von Zuben, F.J. (2008). Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_17

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  • DOI: https://doi.org/10.1007/978-3-540-88138-4_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88137-7

  • Online ISBN: 978-3-540-88138-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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