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European Conference on Genetic Programming (Part of EvoStar)

EuroGP 2022: Genetic Programming pp 245–261Cite as

Accurate and Interpretable Representations of Environments with Anticipatory Learning Classifier Systems

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13223))

Abstract

Anticipatory Learning Classifier Systems (ALCS) are rule-based machine learning algorithms that can simultaneously develop a complete representation of their environment and a decision policy based on this representation to solve their learning tasks. This paper introduces BEACS (Behavioral Enhanced Anticipatory Classifier System) in order to handle non-deterministic partially observable environments and to allow users to better understand the environmental representations issued by the system. BEACS is an ALCS that enhances and merges Probability-Enhanced Predictions and Behavioral Sequences approaches used in ALCS to handle such environments. The Probability-Enhanced Predictions consist in enabling the anticipation of several states, while the Behavioral Sequences permits the construction of sequences of actions. The capabilities of BEACS have been studied on a thorough benchmark of 23 mazes and the results show that BEACS can handle different kinds of non-determinism in partially observable environments, while describing completely and more accurately such environments. BEACS thus provides explanatory insights about created decision policies and environmental representations.

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References

  1. Bagnall, A.J., Zatuchna, Z.V.: On the classification of maze problems. In: Bull, L., Kovacs, T. (eds.) Foundations of Learning Classifier Systems. Studies in Fuzziness and Soft Computing, pp. 305–316. Springer, Heidelberg (2005). https://doi.org/10.1007/11319122_12

    Chapter  Google Scholar 

  2. Butz, A.M.V., Goldberg, B.D.E., Stolzmann, C.W.: The anticipatory classifier system and genetic generalization. Nat. Comput. 1, 427–467 (2002). https://doi.org/10.1023/A:1021330114221

    Article  MathSciNet  MATH  Google Scholar 

  3. Butz, M.V.: Biasing exploration in an anticipatory learning classifier system. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 3–22. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-48104-4_1

    Chapter  MATH  Google Scholar 

  4. Butz, M.V., Goldberg, D.E.: Generalized state values in an anticipatory learning classifier system. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS (LNAI), vol. 2684, pp. 282–301. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45002-3_16

    Chapter  Google Scholar 

  5. Butz, M.V., Goldberg, D.E., Stolzmann, W.: Probability-enhanced predictions in the anticipatory classifier system. In: Luca Lanzi, P., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2000. LNCS (LNAI), vol. 1996, pp. 37–51. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44640-0_4

    Chapter  Google Scholar 

  6. Butz, M.V., Stolzmann, W.: An algorithmic description of ACS2. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2001. LNCS (LNAI), vol. 2321, pp. 211–229. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-48104-4_13

    Chapter  Google Scholar 

  7. Fagerland, M.W., Sandvik, L.: Performance of five two-sample location tests for skewed distributions with unequal variances. Contemp. Clin. Trials 30, 490–496 (2009)

    Article  Google Scholar 

  8. Gérard, P., Meyer, J.A., Sigaud, O.: Combining latent learning with dynamic programming in the modular anticipatory classifier system. Eur. J. Oper. Res. 160(3), 614–637 (2005)

    Article  Google Scholar 

  9. Gerard, P., Stolzmann, W., Sigaud, O.: YACS: a new learning classifier system using anticipation. Soft Comput. 6, 216–228 (2002). https://doi.org/10.1007/s005000100117

    Article  MATH  Google Scholar 

  10. Hasselt, H.: Double q-learning. In: Advances in neural information processing systems, pp. 2613–2621 (2010)

    Google Scholar 

  11. Hoffmann, J.: Anticipatory behavioral control. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS (LNAI), vol. 2684, pp. 44–65. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45002-3_4

    Chapter  Google Scholar 

  12. Kozlowski, N., Unold, O.: Investigating exploration techniques for ACS in discretized real-valued environments. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1765–1773 (2020)

    Google Scholar 

  13. Kozłowski, N., Unold, O.: Anticipatory classifier system with average reward criterion in discretized multi-step environments. Appl. Sci. 11(3), 1098 (2021)

    Article  Google Scholar 

  14. Métivier, M., Lattaud, C.: Anticipatory classifier system using behavioral sequences in non-Markov environments. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2002. LNCS (LNAI), vol. 2661, pp. 143–162. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-40029-5_9

    Chapter  Google Scholar 

  15. Orhand, R., Jeannin-Girardon, A., Parrend, P., Collet, P.: BACS: a thorough study of using behavioral sequences in ACS2. In: Bäck, T., et al. (eds.) PPSN 2020, Part I. LNCS, vol. 12269, pp. 524–538. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58112-1_36

    Chapter  Google Scholar 

  16. Orhand, R., Jeannin-Girardon, A., Parrend, P., Collet, P.: PEPACS: integrating probability-enhanced predictions to ACS2. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1774–1781 (2020)

    Google Scholar 

  17. Orhand, R., Jeannin-Girardon, A., Parrend, P., Collet, P.: Explainability and performance of anticipatory learning classifier systems in non-deterministic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 163–164 (2021)

    Google Scholar 

  18. Stolzmann, W.: An introduction to anticipatory classifier systems. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 175–194. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45027-0_9

    Chapter  Google Scholar 

  19. Stolzmann, W., Butz, M., Hoffmann, J., Goldberg, D.: First cognitive capabilities in the anticipatory classifier system, February 2000

    Google Scholar 

  20. Tan, J., Moore, J., Urbanowicz, R.: Rapid rule compaction strategies for global knowledge discovery in a supervised learning classifier system. In: Artificial Life Conference Proceedings, vol. 13, pp. 110–117 (2013)

    Google Scholar 

  21. Unold, O., Rogula, E., Kozłowski, N.: Introducing action planning to the anticipatory classifier system ACS2. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds.) CORES 2019. AISC, vol. 977, pp. 264–275. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-19738-4_27

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Icube Laboratory - UMR 7357, 300 bd Sébastien Brant, 67412, Illkirch, France

    Romain Orhand, Anne Jeannin-Girardon, Pierre Parrend & Pierre Collet

  2. University of Strasbourg, 4 rue Blaise Pascal, 67081, Strasbourg, France

    Romain Orhand, Anne Jeannin-Girardon & Pierre Collet

  3. EPITA, 14-16 Rue Voltaire, 94270, Le Kremlin-Bicêtre, France

    Pierre Parrend

Authors
  1. Romain Orhand
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  2. Anne Jeannin-Girardon
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  3. Pierre Parrend
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  4. Pierre Collet
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Corresponding author

Correspondence to Romain Orhand .

Editor information

Editors and Affiliations

  1. University of Trieste, Trieste, Italy

    Eric Medvet

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Gisele Pappa

  3. Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

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Orhand, R., Jeannin-Girardon, A., Parrend, P., Collet, P. (2022). Accurate and Interpretable Representations of Environments with Anticipatory Learning Classifier Systems. In: Medvet, E., Pappa, G., Xue, B. (eds) Genetic Programming. EuroGP 2022. Lecture Notes in Computer Science, vol 13223. Springer, Cham. https://doi.org/10.1007/978-3-031-02056-8_16

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  • DOI: https://doi.org/10.1007/978-3-031-02056-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02055-1

  • Online ISBN: 978-3-031-02056-8

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