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Integrating Factorization Ranked Features in MCTS: An Experimental Study

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Computer Games (CGW 2016, GIGA 2016)

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

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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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References

  1. Browne, C., Powley, E., Whitehouse, D., Lucas, S., Cowling, P., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intellig. AI Games 4(1), 1–43 (2012)

    Article  Google Scholar 

  2. Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: Herik, H.J., Ciancarini, P., Donkers, H.H.L.M.J. (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75538-8_7

    Chapter  Google Scholar 

  3. Coulom, R.: Computing “Elo ratings” of move patterns in the game of Go. ICGA J. 30(4), 198–208 (2007)

    Google Scholar 

  4. Enzenberger, M., Müller, M.: Fuego (2008–2015). http://fuego.sourceforge.net

  5. 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 

  6. Friedenbach, K.J.: Abstraction hierarchies: a model of perception and cognition in the game of Go. Ph.D. thesis, University of California, Santa Cruz (1980)

    Google Scholar 

  7. Gelly, S., Silver, D.: Combining online and offline knowledge in UCT. In: Proceedings of the 24th International Conference on Machine Learning (ICML 2007), pp. 273–280. ACM (2007)

    Google Scholar 

  8. Hunter, D.R.: MM algorithms for generalized Bradley-Terry models. Ann. Stat. 32(1), 384–406 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  9. 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). doi:10.1007/11871842_29

    Chapter  Google Scholar 

  10. Müller, M.: Computer Go. Artif. Intell. 134(1–2), 145–179 (2002)

    Article  MATH  Google Scholar 

  11. Rosin, C.: Multi-armed bandits with episode context. Ann. Math. Artif. Intell. 61(3), 203–230 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  12. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    Article  Google Scholar 

  13. Stern, D., Herbrich, R., Graepel, T.: Bayesian pattern ranking for move prediction in the game of Go. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 873–880. ACM (2006)

    Google Scholar 

  14. Wistuba, M., Schmidt-Thieme, L.: Move prediction in Go – modelling feature interactions using latent factors. In: Timm, I.J., Thimm, M. (eds.) KI 2013. LNCS (LNAI), vol. 8077, pp. 260–271. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40942-4_23

    Chapter  Google Scholar 

  15. Xiao, C., Müller, M.: Factorization ranking model for move prediction in the game of Go. In: AAAI, pp. 1359–1365 (2016)

    Google Scholar 

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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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