Integrating Factorization Ranked Features in MCTS: An Experimental Study

  • Chenjun XiaoEmail author
  • Martin Müller
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 705)


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.


  1. 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)CrossRefGoogle Scholar
  2. 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 CrossRefGoogle Scholar
  3. 3.
    Coulom, R.: Computing “Elo ratings” of move patterns in the game of Go. ICGA J. 30(4), 198–208 (2007)Google Scholar
  4. 4.
    Enzenberger, M., Müller, M.: Fuego (2008–2015).
  5. 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)CrossRefGoogle Scholar
  6. 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. 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. 8.
    Hunter, D.R.: MM algorithms for generalized Bradley-Terry models. Ann. Stat. 32(1), 384–406 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 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 CrossRefGoogle Scholar
  10. 10.
    Müller, M.: Computer Go. Artif. Intell. 134(1–2), 145–179 (2002)CrossRefzbMATHGoogle Scholar
  11. 11.
    Rosin, C.: Multi-armed bandits with episode context. Ann. Math. Artif. Intell. 61(3), 203–230 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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. 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 CrossRefGoogle Scholar
  15. 15.
    Xiao, C., Müller, M.: Factorization ranking model for move prediction in the game of Go. In: AAAI, pp. 1359–1365 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computing ScienceUniversity of AlbertaEdmontonCanada

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