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Towards Human-Competitive Game Playing for Complex Board Games with Genetic Programming

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Artificial Evolution (EA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9554))

Abstract

Recent works have shown that Genetic Programming (GP) can be quite successful at evolving human-competitive strategies for games ranging from classic board games, such as chess, to action video games. However to our knowledge GP was never applied to modern complex board games, so-called eurogames, such as Settlers of Catan, i.e. board games that typically involve four characteristics: they are non zero-sum games, multiplayer, with hidden information and random elements. In this work we study how GP can evolve artificial players from low level attributes of a eurogame named “7 Wonders”, that features all the characteristics of this category. We show that GP can evolve competitive artificial intelligence (AI) players against human-designed AI or against Monte Carlo Tree Search, a standard in automatic game playing.

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Notes

  1. 1.

    While the rule allows 2 player games, these are played by simulating a 3rd “dumb” player.

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Author information

Authors and Affiliations

  1. LISIC, ULCO, University of Lille–Nord de France, Calais, France

    Denis Robilliard & Cyril Fonlupt

Authors
  1. Denis Robilliard
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  2. Cyril Fonlupt
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Corresponding author

Correspondence to Denis Robilliard .

Editor information

Editors and Affiliations

  1. Université Lyon 1, Lyon, France

    Stéphane Bonnevay

  2. Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  3. Université de Tours, Tours, France

    Nicolas Monmarché

  4. INRA, Thirverval-Grignon, France

    Evelyne Lutton

  5. Inria Saclay, Université Paris-Sud, Orsay, France

    Marc Schoenauer

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Robilliard, D., Fonlupt, C. (2016). Towards Human-Competitive Game Playing for Complex Board Games with Genetic Programming. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2015. Lecture Notes in Computer Science(), vol 9554. Springer, Cham. https://doi.org/10.1007/978-3-319-31471-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-31471-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31470-9

  • Online ISBN: 978-3-319-31471-6

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