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International Workshop on Learning Classifier Systems

IWLCS 1999: Learning Classifier Systems pp 175–194Cite as

An Introduction to Anticipatory Classifier Systems

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1813))

Abstract

Anticipatory Classifier Systems (ACS) are classifier systems that learn by using the cognitive mechanism of anticipatory behavioral control which was introduced in cognitive psychology by Hoffmann [4]. They can learn in deterministic multi-step environments.1 A stepwise introduction to ACS is given. We start with the basic algorithm and apply it in simple “woods” environments. It will be shown that this algorithm can only learn in a special kind of deterministic multi-step environments. Two extensions are discussed. The first one enables an ACS to learn in any deterministic multi-step environment. The second one allows an ACS to deal with a special kind of non-Markov state.

Butz, Goldberg & Stolzmann [2] show that ACS can also learn in deterministic single-step environments with a perceptual causality in its successive states.

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References

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Author information

Authors and Affiliations

  1. Institute for Psychology, University of Wuerzburg, Germany

    Wolfgang Stolzmann

Authors
  1. Wolfgang Stolzmann
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica ed Informatzione, Politecnico di Milano, Piazza Leonardo da Vinci n. 32, 20133, Milano, Italy

    Pier Luca Lanzi

  2. Institut für Psychologie III, Universität Würzburg, Röntgenring 11, 97070, Würzburg, Germany

    Wolfgang Stolzmann

  3. Prediction Dynamics, Concord, MA, 01742, USA

    Stewart W. Wilson

  4. Department of General Engineering, University of Illinois at Urbana-Champaign, USA

    Stewart W. Wilson

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© 2000 Springer-Verlag Berlin Heidelberg

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Stolzmann, W. (2000). An Introduction to Anticipatory Classifier Systems. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS 1999. Lecture Notes in Computer Science(), vol 1813. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45027-0_9

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  • DOI: https://doi.org/10.1007/3-540-45027-0_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67729-1

  • Online ISBN: 978-3-540-45027-6

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