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Monte-Carlo Tree Reductions for Stochastic Games

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Book cover Technologies and Applications of Artificial Intelligence (TAAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8916))

Abstract

Monte-Carlo Tree Search (MCTS) is a powerful paradigm for perfect information games. When considering stochastic games, the tree model that represents the game has to take chance and a huge branching factor into account. As effectiveness of MCTS may decrease in such a setting, tree reductions may be useful. Chance-nodes are a way to deal with random events. Move-groups are another way to deal efficiently with a large branching factor by regrouping nodes. Group-nodes are regrouping only reveal moves and enable a choice between reveal moves and classical moves. We present various policies to use such reductions for the stochastic game Chinese Dark Chess. Move-groups, chance-nodes and group-nodes are compared.

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Author information

Authors and Affiliations

  1. LIASD, Université de Paris 8, France

    Nicolas Jouandeau

  2. LAMSADE, Université Paris-Dauphine, France

    Tristan Cazenave

Authors
  1. Nicolas Jouandeau
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  2. Tristan Cazenave
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Da’an Dist., 106, Taipei City, Taiwan

    Shin-Ming Cheng

  2. Department of Information Management, Tamkang University, No. 151, Yingzhuan Rd., Danshui Dist., 25137, New Taipei City, Taiwan

    Min-Yuh Day

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© 2014 Springer International Publishing Switzerland

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Jouandeau, N., Cazenave, T. (2014). Monte-Carlo Tree Reductions for Stochastic Games. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_22

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  • DOI: https://doi.org/10.1007/978-3-319-13987-6_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13986-9

  • Online ISBN: 978-3-319-13987-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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