D. E. Knuth and R. W. Moore, An analysis of alpha-beta pruning, Artificial Intelligence, vol.6, issue.4, pp.293-326, 1976.
DOI : 10.1016/0004-3702(75)90019-3

R. Coulom, Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, Proceedings of the 5th International Conference on Computers and Games, pp.72-83, 2006.
DOI : 10.1007/978-3-540-75538-8_7
URL : https://hal.archives-ouvertes.fr/inria-00116992

B. Bouzy, Associating shallow and selective global tree search with Monte Carlo for 9x9 Go, 4rd Computer and Games Conference, 2004.

T. Cazenave, A Phantom-Go Program, Advances in Computer Games, ser. Lecture Notes in Computer Science, H. van den Herik, pp.120-125, 2006.
DOI : 10.1007/11922155_9

T. Cazenave and J. Borsboom, Golois wins phantom go tournament, ICGA Journal, vol.30, issue.3, pp.165-166, 2007.

T. Cazenave, Nested monte-carlo search, pp.456-461, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01436286

T. Cazenave and A. Saffidine, Score Bounded Monte-Carlo Tree Search, Proceedings of the 7th International Conference on Computers and Games, ser. CG'10, pp.93-104, 2011.
DOI : 10.1007/978-3-642-17928-0_9

D. Billings, L. P. Castillo, J. Schaeffer, and D. Szafron, Using probabilistic knowledge and simulation to play poker, AAAI/IAAI, pp.697-703, 1999.

G. Tesauro and G. R. Galperin, On-line policy improvement using monte-carlo search, NIPS, pp.1068-1074, 1996.

S. Gelly and D. Silver, Combining online and offline knowledge in UCT, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.273-280, 2007.
DOI : 10.1145/1273496.1273531
URL : https://hal.archives-ouvertes.fr/inria-00164003

]. R. Coulom, Computing Elo ratings of move patterns in the game of Go, ICGA Journal, vol.30, issue.4, pp.198-208, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00149859

D. Silver and G. Tesauro, Monte-Carlo simulation balancing, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.945-952, 2009.
DOI : 10.1145/1553374.1553495

S. Huang, R. Coulom, and S. Lin, Monte-Carlo Simulation Balancing in Practice, Computers and Games, ser. Lecture Notes in Computer Science, H. J. van den Herik, H. Iida, and A. Plaat, pp.81-92, 2010.
DOI : 10.1007/978-3-642-17928-0_8

I. S. Chaslot, M. H. Winands, and H. Van-den-herik, Parameter tuning by the cross-entropy method, European workshop on reinforcement learning, 2008.

S. Gelly, W. Yizao, R. Munos, and O. Teytaud, Modification of uct with patterns in monte-carlo go, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00117266

P. Baudi? and J. Gailly, PACHI: State of the Art Open Source Go Program, Proc. 13th Int. Conf. Adv. Comput. Games, pp.24-38, 2011.
DOI : 10.1007/978-3-642-31866-5_3

J. M. Robson, The complexity of go, IFIP Congress, pp.413-417, 1983.

M. D. Grigoriadis and L. G. Khachiyan, A sublinear-time randomized approximation algorithm for matrix games, Operations Research Letters, vol.18, issue.2, pp.53-58, 1995.
DOI : 10.1016/0167-6377(95)00032-0

P. Auer, N. Cesa-bianchi, Y. Freund, and R. E. Schapire, Gambling in a rigged casino: The adversarial multi-armed bandit problem, Proceedings of IEEE 36th Annual Foundations of Computer Science, pp.322-331, 1995.
DOI : 10.1109/SFCS.1995.492488

A. Bayer, D. Bump, E. B. Daniel, D. Denholm, J. Dumonteil et al., Gnu go 3.8 documentation, 2008.