Abstract
We propose a new randomized game-tree search algorithm based on Bayesian Approach. It consists of two main concepts; (1) using multiple game-tree search with a randomized evaluation function as simulations, (2) treating evaluated values as probability distribution and propagating it through the game-tree using the Bayesian Approach concept. Proposed method is focusing on applying to tactical games such as Shogi, in which MCTS is not currently effective. We apply the method for Shogi using a top-level computer player application which is constructed with many domain-specific search techniques. Through large amount of self-play evaluations, we conclude our method can achieve good win ratio against an ordinary game-tree search based player when enough computing resource is available. We also precisely examine performance behaviors of the method, and depict designing directions.
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References
Baum, E.B., Smith, W.D.: A bayesian approach to relevance in game playing. Artificial Intelligence 97(1-2), 195–242 (1997)
Coulom, R.: Efficient selectivity and backup operators in monte-carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M(J.) (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007)
Gelly, S., Wang, Y., Munos, R., Teytaud, O.: Modification of UCT with Patterns in Monte-Carlo Go. Tech. Rep. RR-6062, INRIA (2006), http://hal.inria.fr/inria-00117266
Hart, J.P., Shogan, A.W.: Semi-greedy heuristics: An empirical study. Operations Research Letters 6(3), 107–114 (1987)
Hoki, K., Kaneko, T., Yokoyama, D., Obata, T., Yamashita, H., Tsuruoka, Y., Ito, T.: A system-design outline of the distributed-shogi-system akara 2010. In: 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 466–471 (July 2013)
Hoki, K., Kaneko, T.: The global landscape of objective functions for the optimization of shogi piece values with a game-tree search. In: van den Herik, H.J., Plaat, A. (eds.) ACG 2011. LNCS, vol. 7168, pp. 184–195. Springer, Heidelberg (2012), http://dx.doi.org/10.1007/978-3-642-31866-5_16
Hoki, K., Muramatsu, M.: Efficiency of three forward-pruning techniques in shogi: Futility pruning, null-move pruning, and late move reduction (LMR). Entertainment Computing 3(3), 51–57 (2012), http://www.sciencedirect.com/science/article/pii/S1875952111000450
Kishimoto, A., Winands, M., Müller, M., Saito, J.T.: Game-tree search using proof numbers: The first twenty years. ICGA Journal 35(3), 131–156 (2012)
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), http://dx.doi.org/10.1007/11871842_29
Kozelek, T.: Methods of mcts and the game arimaa. Charles University, Prague, Faculty of Mathematics and Physics (2009)
Lorentz, R.J.: Amazons discover monte-carlo. In: van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds.) CG 2008. LNCS, vol. 5131, pp. 13–24. Springer, Heidelberg (2008)
Sato, Y., Takahashi, D.: A shogi program based on monte-carlo tree search. In: The 13th Game Programming Workshop (2008) (in Japanese)
Takeuchi, S., Kaneko, T., Yamaguchi, K.: Evaluation function based monte carlo tree search in shogi. In: The 15th Game Programming Workshop, pp. 86–89 (2010) (in Japanese)
Tsuruoka, Y., Yokoyama, D., Chikayama, T.: Game-tree search algorithm based on realization probability. ICGA Journal 25(3), 145–152 (2002)
Winands, M.H.M., Björnsson, Y., Saito, J.-T.: Monte-carlo tree search solver. In: van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds.) CG 2008. LNCS, vol. 5131, pp. 25–36. Springer, Heidelberg (2008), http://dx.doi.org/10.1007/978-3-540-87608-3_3
Winands, M.H.M., Björnsson, Y.: Evaluation function based monte-carlo LOA. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 33–44. Springer, Heidelberg (2010), http://dx.doi.org/10.1007/978-3-642-12993-3_4
Zobrist, A.L.: A new hashing method with application for game playing. ICCA Journal 13(2), 69–73 (1990)
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Yokoyama, D., Kitsuregawa, M. (2014). A Randomized Game-Tree Search Algorithm for Shogi Based on Bayesian Approach. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_81
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DOI: https://doi.org/10.1007/978-3-319-13560-1_81
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