Abstract
3×3 patterns are widely used in Monte-Carlo (MC) Go programs to improve the performance. In this paper, we propose a direct indexing approach to build and use a complete 3×3 pattern library. The contents of the immediate 8 neighboring positions of a board point are coded into a 16-bit string, called surrounding index. The surrounding indices of all board points can be updated incrementally in an efficient way. We propose an effective method to learn the pattern weights from forty thousand professional games. The method converges faster and performs equally well or better than the method of computing “Elo ratings” [4]. The knowledge contained in the pattern library can be efficiently applied to the MC simulations and to the growth of MC search tree. Testing results showed that our method increased the winning rates of Go Intellect against GNU Go on 9×9 games by over 7% taking the tax on the program speed into consideration.
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References
Bouzy, B., Chaslot, G.M.J.-B.: Bayesian Generation and Integration of K-nearest neighbor Patterns for 19×19 Go. In: Kendall, G., Lucas, S. (eds.) IEEE 2005 Symposium on Computational Intelligence in Games, Essex, UK, pp. 176–181 (2005)
Bouzy, B., Chaslot, G.M.J.-B.: Monte-Carlo Go Reinforcement Learning Experiments. In: IEEE 2006 Symposium on Computational Intelligence in Games, Reno, USA, pp. 187–194 (2006)
Chen, K., Zhang, P.: Monte-Carlo Go with Knowledge-guided Simulations. ICGA Journal 31(2), 67–76 (2008)
Coulom, R.: Computing “Elo Ratings” of Move Patterns in the Game of Go. ICGA Journal 30(4), 198–208 (2007)
Gelly, S., Wang, Y., Munos, R., Teytaud, O.: Modification of UCT with Patterns in Monte-Carlo Go. Technical Report 6062, INRIA (2006)
Gelly, S., Silver, D.: Combining Online and Offline Knowledge in UCT. In: Ghahramani, Z. (ed.) Proceedings of the International Conference of Machine Learning (ICML 2007), pp. 273–280 (2007)
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)
Silver, D., Sutton, R.S., Müller, M.: Reinforcement Learning of Local Shape in the Game of Go. In: 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 1053–1058 (2007)
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, Pittsburgh, PA (2006)
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Chen, KH., Du, D., Zhang, P. (2008). A Fast Indexing Method for Monte-Carlo Go. In: van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds) Computers and Games. CG 2008. Lecture Notes in Computer Science, vol 5131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87608-3_9
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DOI: https://doi.org/10.1007/978-3-540-87608-3_9
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