Abstract
Monte-Carlo Tree Search is a popular method to implement computer programs for board games, and its performance can be significantly improved by including static knowledge about the game, for example in the formof patterns learned from game records. Finding the right pattern shapes is still an open problem, and we propose in this paper an evolutionary-like method to optimize the pattern shapes. We avoid direct optimization through the heavy Monte-Carlo framework by using instead the performance of a machine-learning algorithm as an early indicator of the quality of the pattern shapes. We have implemented this general method on the specific case of the game of Othello. The final pattern shapes obtained after optimization would be hard to find manually, and they greatly improve the strength of our Othello program.
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References
Baier, H., Winands, M.H.M.: Monte-carlo tree search and minimax hybrids. In: Proceedings of the Conference on Computational Intelligence in Games 2013, pp. 129–137. IEEE (2013)
Bouzy, B., Chaslot, G.: Bayesian generation and integration of k-nearest-neighbor patterns for 19x19 go. In: IEEE 2005 Symposium on Computational Intelligence in Games, pp. 1019–1025. IEEE (2005)
Buro, M.: The evolution of strong othello programs. In: Nakatsu, R., Hoshino, J. (eds.) Entertainment Computing. IFIP, vol. 112, pp. 81–88. Springer, Boston (2003)
Cameron, B., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games 4(1), 1–43 (2012)
Coulom, R.: Computing elo ratings of move patterns in the game of go. Int. Comp. Games Assoc. J. 30(4), 198–208 (2007)
Ikeda, K., Viennot, S.: Efficiency of static knowledge bias in monte-carlo tree search. In: van den Herik, H.J., Iida, H., Plaat, A. (eds.) CG 2013. LNCS, vol. 8427, pp. 26–38. Springer, Heidelberg (2014)
Nguyen, H.Q., Ikeda, K.: Evaluation of pattern shapes in board games before machine learning. International Journal of Electrical Engineering 20(2), 39–49 (2013)
Silver, D., Muller, M.: Reinforment learning of local shape in the game of go. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 1053–1058. Springer (2007)
Stern, D., Herbrich, R.: Bayesian pattern ranking for move prediction in the game of go. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 873–880 (2006)
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Nguyen, H., Viennot, S., Ikeda, K. (2015). Fast Optimization of the Pattern Shapes in Board Games with Simulated Annealing. In: Nguyen, VH., Le, AC., Huynh, VN. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-319-11680-8_26
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DOI: https://doi.org/10.1007/978-3-319-11680-8_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11679-2
Online ISBN: 978-3-319-11680-8
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