Advertisement

An Introduction to Anticipatory Classifier Systems

  • Wolfgang Stolzmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Goal State Learn Classifier System Knowledge Number Classifier List Basic Learning Algorithm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Barry, Alwyn (1999). Aliasing in XCS and the Consequtive State Problem. In Banzaf, W. et al. (editors). Proceedings of the Genetic and Evolutionary Computation Conference GECCO 99, July 13–17,1999 Orlando, Florida. Volume 1. San Francisco, CA: Morgan Kaufmann. 19–34.Google Scholar
  2. [2]
    Butz, M., Goldberg, D., & Stolzmann, W. (1999). New Challenges for an Anticipatory Classifier System: Some Hard Problems and Possible Solutions. IlliGAL Report No. 99019. Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-ChampaignGoogle Scholar
  3. [3]
    Cliff, Dave and Ross, Susi (1995). Adding Temporary Memory to ZCS. Adaptive Behavior Vol.3, No. 2, 101–150.CrossRefGoogle Scholar
  4. [4]
    Hoffmann, Joachim (1993). Vorhersage und Erkenntnis. Göttingen: Hogrefe.Google Scholar
  5. [5]
    Holland, J. H. (1985). Properties of the bucket brigade algorithm. In John J. Grefenstette, editor. Proceedings of the 1st International Conference on Genetic Algorithms and their Applications (ICGA85). Lawrence Erlbaum Associates: Pittsburgh, PA, July 1985. 1–7.Google Scholar
  6. [6]
    Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading, Massachusetts: Addison-Wesley.zbMATHGoogle Scholar
  7. [7]
    Lanzi, Pier Luca (1998). An Analysis of the Memory Mechanism of XCSM. In Koza, John R. et al. (editors). Genetic Programming 1998: Proceedings of the Third Annual Conference, July 22–25, 1998, University of Wisconsin, Madison, Wisconsin. San Francisco, CA: Morgan Kaufmann. 643–651.Google Scholar
  8. [8]
    Lanzi, P. L., & Colombetti, M. (1999). An Extension to the XCS Classifier System for Stochastic Environments. In Banzaf, W. et al. (editors). Proceedings of the Genetic and Evolutionary Computation Conference GECCO 99, July 13–17,1999 Orlando, Florida. Volume 1. San Francisco, CA: Morgan Kaufmann. 353–360.Google Scholar
  9. [9]
    Lanzi, P. L., & Wilson, S. W. (1999). Optimal Classifier System Performance in Non-Markov Environments. Technical Report N. 99.36, Politecnico di Milano (submitted to Evolutionary Computation).Google Scholar
  10. [10]
    Smith, R. E. (1994). Memory exploitation in learning classifier systems. Evolutionary Computation 2(3). 199–220.CrossRefGoogle Scholar
  11. [11]
    Stolzmann, Wolfgang (1998). Anticipatory Classifier Systems. In Koza, John R. et al. (editors). Genetic Programming 1998: Proceedings of the Third Annual Conference, July 22–25, 1998, University of Wisconsin, Madison, Wisconsin. San Francisco, CA: Morgan Kaufmann. 658–664.Google Scholar
  12. [12]
    Tolman, Edward C. (1932). Purposive behavior in animals and men. New York: Appleton.Google Scholar
  13. [13]
    Widrow, B., & Hoff, M. (1960). Adaptive switching circuits. Western Electronic Show and Convention, 4. 96–104.Google Scholar
  14. [14]
    Wilson, S. W. (1985). Knowledge growth in an artificial animat. In L.E. Associates (Ed.). Proceedings of the First International Conference on Genetic Algorithms and Their Applications. 16–23Google Scholar
  15. [15]
    Wilson, S. W. (1994). ZCS: a zeroth level classifier system. Evolutionary Computation 1(2). 1–18CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Wolfgang Stolzmann
    • 1
  1. 1.Institute for PsychologyUniversity of WuerzburgGermany

Personalised recommendations