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Artificial Intelligence for Games

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Abstract

This chapter presents the classical alpha-beta algorithm and several variants, Monte Carlo Tree Search which is at the origin of recent progresses in many games, techniques used in video games and puzzles, and retrograde analysis which performs perfect play in endgames.

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Authors and Affiliations

  1. LIPADE UFR de mathématiques et d’informatique, Université René Descartes, 45 rue des Saints-Pères, 75270, Paris Cedex 06, France

    Bruno Bouzy

  2. LAMSADE, Université Paris-Dauphine, Place du Maréchal de Lattre de Tassigny, 75775, Paris Cedex 16, France

    Tristan Cazenave

  3. Sorbonne Université, LIP6 Boîte courrier 169 Couloir 25-26, Étage 4, Bureau 412 – 4 place Jussieu, 75252, Paris Cedex 05, France

    Vincent Corruble

  4. TAU, Inria, Bât .650, Rue Noetzlin, 91190, Gif-sur-Yvette, France

    Olivier Teytaud

Authors
  1. Bruno Bouzy
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  2. Tristan Cazenave
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  3. Vincent Corruble
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  4. Olivier Teytaud
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Corresponding author

Correspondence to Bruno Bouzy .

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Editors and Affiliations

  1. CRIL-CNRS, Université d’Artois and Institut Universitaire de France, Lens, France

    Pierre Marquis

  2. Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France

    Odile Papini

  3. IRIT, CNRS and Université Paul Sabatier, Toulouse, France

    Henri Prade

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Bouzy, B., Cazenave, T., Corruble, V., Teytaud, O. (2020). Artificial Intelligence for Games. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06167-8_11

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