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Workshop on Computer Games

International Workshop on General Intelligence in Game-Playing Agents

CGW 2016, GIGA 2016: Computer Games pp 34–43Cite as

Integrating Factorization Ranked Features in MCTS: An Experimental Study

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 705))

Abstract

Recently, Factorization Bradley-Terry (FBT) model is introduced for fast move prediction in the game of Go. It has been shown that FBT outperforms the state-of-the-art fast move prediction system of Latent Factor Ranking (LFR). In this paper, we investigate the problem of integrating feature knowledge learned by FBT model in Monte Carlo Tree Search. We use the open source Go program Fuego as the test platform. Experimental results show that the FBT knowledge is useful in improving the performance of Fuego.

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Author information

Authors and Affiliations

  1. Computing Science, University of Alberta, Edmonton, Canada

    Chenjun Xiao & Martin Müller

Authors
  1. Chenjun Xiao
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  2. Martin Müller
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Corresponding author

Correspondence to Chenjun Xiao .

Editor information

Editors and Affiliations

  1. LAMSADE, Université Paris-Dauphine, Paris Cedex 16, France

    Tristan Cazenave

  2. Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, Limburg, The Netherlands

    Mark H.M. Winands

  3. Institute for Artificial Intelligence, Universität Bremen, TAB, Bremen, Bremen, Germany

    Stefan Edelkamp

  4. Reykjavik University, Reykjavik, Iceland

    Stephan Schiffel

  5. The University of New South Wales, Sydney, New South Wales, Australia

    Michael Thielscher

  6. Department of Computer Science and Engineering, New York University, Brooklyn, New York, USA

    Julian Togelius

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Xiao, C., Müller, M. (2017). Integrating Factorization Ranked Features in MCTS: An Experimental Study. In: Cazenave, T., Winands, M., Edelkamp, S., Schiffel, S., Thielscher, M., Togelius, J. (eds) Computer Games. CGW GIGA 2016 2016. Communications in Computer and Information Science, vol 705. Springer, Cham. https://doi.org/10.1007/978-3-319-57969-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-57969-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57968-9

  • Online ISBN: 978-3-319-57969-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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