Creating an Upper-Confidence-Tree program for Havannah

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6048 LNCS, Issue , 2010, Pages 65-74

  Teytaud, Fabien ^a   Teytaud, Olivier ^a  

^a CNRS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONNECTION GAMES; MONTE CARLO; TREE SEARCH; UPPER CONFIDENCE BOUND;

COMPUTER SOFTWARE;

EID: 77953773563     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12993-3_7     Document Type: Conference Paper

References (21)

1. Wikipedia, Havannah (2009)
2. Schmittberger, R.W.: New Rules for Classic Games. Wiley, Chichester (1992)
3. Chaslot, G., Saito, J.T., Bouzy, B., Uiterwijk, J.W.H.M., van den Herik, H.J.: Monte-Carlo Strategies for Computer Go. In: Schobbens, P.Y., Vanhoof, W., Schwanen, G. (eds.) Proceedings of the 18th BeNeLux Conference on Artificial Intelligence, Namur, Belgium, pp. 83-91 (2006)
4. 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)
5. Kocsis, L., Szepesvari, 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)
6. Wang, Y., Gelly, S.: Modifications of UCT and sequence-like simulations for Monte- Carlo Go. In: IEEE Symposium on Computational Intelligence and Games, Honolulu, Hawaii, pp. 175-182 (2007)
7. Bruegmann, B.: Monte carlo go (1993) (Unpublished)
8. Gelly, S., Silver, D.: Combining online and offline knowledge in uct. In: ICML 2007: Proceedings of the 24th international conference on Machine learning, New York, NY, USA, pp. 273-280. ACM Press, New York (2007)
9. Coulom, R.: Computing elo ratings of move patterns in the game of go. In: Computer Games Workshop, Amsterdam, The Netherlands (2007)
10. Chaslot, G., Winands, M., Uiterwijk, J., van den Herik, H., Bouzy, B.: Progressive strategies for monte-carlo tree search. In: Wang, P. (ed.) Proceedings of the 10th Joint Conference on Information Sciences (JCIS 2007), pp. 655-661. World Scientific Publishing Co. Pte. Ltd., Singapore (2007)
11. Lee, C.S., Wang, M.H., Chaslot, G., Hoock, J.B., Rimmel, A., Teytaud, O., Tsai, S.R., Hsu, S.C., Hong, T.P.: The computational intelligence of mogo revealed in taiwan's computer go tournaments. IEEE Transactions on Computational Intelligence and AI in Games (2009) (accepted)
12. Gelly, S., Hoock, J.B., Rimmel, A., Teytaud, O., Kalemkarian, Y.: The parallelization of monte-carlo planning. In: Proceedings of the International Conference on Informatics in Control, Automation and Robotics (ICINCO 2008), pp. 198-203 (2008) (to appear)
13. Chaslot, G., Winands, M., van den Herik, H.: Parallel Monte-Carlo Tree Search. In: van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds.) CG 2008. LNCS, vol.5131. Springer, Heidelberg (2008)
14. Cazenave, T., Jouandeau, N.: On the parallelization of UCT. In: Proceedings of CGW 2007, pp. 93-101 (2007)
15. Kato, H., Takeuchi, I.: Parallel monte-carlo tree search with simulation servers. In: 13th Game Programming Workshop, GPW 2008 (November 2008)
16. Audouard, P., Chaslot, G., Hoock, J.B., Perez, J., Rimmel, A., Teytaud, O.: Grid coevolution for adaptive simulations; application to the building of opening books in the game of go. In: Proceedings of EvoGames (2009)
17. Lai, T., Robbins, H.: Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics 6, 4-22 (1985)
18. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite time analysis of the multiarmed bandit problem. Machine Learning 47(2/3), 235-256 (2002)
19. Audibert, J.Y., Munos, R., Szepesvari, C.: Use of variance estimation in the multiarmed bandit problem. In: NIPS 2006 Workshop on On-line Trading of Exploration and Exploitation (2006)
20. Mnih, V., Szepesv́ari, C., Audibert, J.Y.: Empirical Bernstein stopping. In: ICML 2008: Proceedings of the 25th international conference on Machine learning, New York, NY, USA, pp. 672-679. ACM, New York (2008)
21. Wang, Y., Audibert, J.Y., Munos, R.: Algorithms for infinitely many-armed bandits. In: Advances in Neural Information Processing Systems., vol.21 (2008)