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Efficiency of Static Knowledge Bias in Monte-Carlo Tree Search

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Computers and Games (CG 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8427))

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Abstract

Monte-Carlo methods are currently the best known algorithms for the game of Go. It is already shown that Monte-Carlo simulations based on a probability model containing static knowledge of the game are more efficient than random simulations. Some programs also use such probability models in the tree search policy to limit the search to a subset of the legal moves or to bias the search. However, this aspect is not so well documented. In this paper, we describe more precisely how static knowledge can be used to improve the tree search policy. We show experimentally the efficiency of the proposed method by a large number of games played against open source Go programs.

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References

  1. Brugmann, B.: Monte Carlo Go. Technical report, Max-Planck Institute of Physics (1993)

    Google Scholar 

  2. Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006)

    Google Scholar 

  3. Coulom, R.: Computing Elo ratings of move patterns in the game of go. J. Int. Comput. Games Assoc. 30(4), 198–208 (2007)

    Google Scholar 

  4. Chaslot, G., Winands, M., Uiterwijk, J., van den Herik, H., Bouzy, B.: Progressive strategies for Monte-Carlo tree search. New Math. Nat. Comput. 4(3), 343–357 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  5. Fotland, D.: Message on the Computer Go mailing list (2009). http://www.mail-archive.com/computer-go@computer-go.org/msg12628.html

  6. Ojima, Y.: Message on the Computer Go mailing list (2009). http://www.mail-archive.com/computer-go@computer-go.org/msg10969.html

  7. Enzenberger, M., Müller, M., Arneson, B., Segal, R.: Fuego - an open-source framework for board games and go engine based on Monte Carlo tree search. IEEE Trans. Comput. Intell. AI Games 2(4), 259–270 (2010)

    Article  Google Scholar 

  8. Chaslot, G., Fiter, C., Hoock, J.-B., Rimmel, A., Teytaud, O.: Adding expert knowledge and exploration in Monte-Carlo tree search. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 1–13. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Gelly, S., Silver, D.: Monte-Carlo tree search and rapid action value estimation in computer go. Artif. Intell. 175(11), 1856–1875 (2011)

    Article  MathSciNet  Google Scholar 

  10. Huang, S.C.: New heuristics for Monte Carlo tree search applied to the game of go. Ph.D. thesis, National Taiwan Normal University (2011)

    Google Scholar 

  11. Baudis, P.: MCTS with information sharing. Master thesis, Charles University in Prague (2011)

    Google Scholar 

  12. Baudiš, P., Gailly, J.: PACHI: state of the art open source go program. In: van den Herik, H.J., Plaat, A. (eds.) ACG 2011. LNCS, vol. 7168, pp. 24–38. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  13. Yoshizoe, K., Yamashita, H.: Computer Go Theory and Practice of Monte-Carlo Method (2012, in Japanese). http://www.yss-aya.com/book2011/

  14. Ikeda,K., Viennot, S.: Production of various strategies and position control for Monte-Carlo go - entertaining human players. In: IEEE Conference on Computational Intelligence in Games, pp. 145–152 (2013)

    Google Scholar 

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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Kokolo Ikeda & Simon Viennot

Authors
  1. Kokolo Ikeda
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  2. Simon Viennot
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Corresponding author

Correspondence to Simon Viennot .

Editor information

Editors and Affiliations

  1. Tilburg University, Tilburg, The Netherlands

    H. Jaap van den Herik

  2. JAIST, Ishikawa, Japan

    Hiroyuki Iida

  3. Tilburg University, Tilburg, The Netherlands

    Aske Plaat

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Ikeda, K., Viennot, S. (2014). Efficiency of Static Knowledge Bias in Monte-Carlo Tree Search. In: van den Herik, H., Iida, H., Plaat, A. (eds) Computers and Games. CG 2013. Lecture Notes in Computer Science(), vol 8427. Springer, Cham. https://doi.org/10.1007/978-3-319-09165-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-09165-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09164-8

  • Online ISBN: 978-3-319-09165-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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