J. Audibert, R. Munos, and C. Szepesvari, Use of variance estimation in the multi-armed bandit problem, NIPS 2006 Workshop on On-line Trading of Exploration and Exploitation, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00203496

P. Audouard, G. Chaslot, J. Hoock, J. Perez, A. Rimmel et al., Grid Coevolution for Adaptive Simulations: Application to the Building of Opening Books in the Game of Go, Proceedings of EvoGames, 2009.
DOI : 10.1007/978-3-642-01129-0_36
URL : https://hal.archives-ouvertes.fr/inria-00369783

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite time analysis of the multiarmed bandit problem, Machine Learning, vol.47, issue.2/3, pp.235-256, 2002.
DOI : 10.1023/A:1013689704352

B. Bruegmann, Monte carlo go, 1993.

T. Cazenave and N. Jouandeau, On the parallelization of UCT, Proceedings of CGW07, pp.93-101, 2007.

G. Chaslot, J. Saito, B. Bouzy, J. W. Uiterwijk, and H. J. Van-den-herik, Monte-Carlo Strategies for Computer Go, Proceedings of the 18th BeNeLux Conference on Artificial Intelligence, pp.83-91, 2006.

G. Chaslot, M. Winands, J. Uiterwijk, H. Van-den-herik, and B. Bouzy, Progressive strategies for monte-carlo tree search, Proceedings of the 10th Joint Conference on Information Sciences, pp.655-661, 2007.

G. Chaslot, M. Winands, and H. Van-den-herik, Parallel Monte-Carlo Tree Search, Proceedings of the Conference on Computers and Games, 2008.
DOI : 10.1007/978-3-540-87608-3_6

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

R. Coulom, Computing elo ratings of move patterns in the game of go, Computer Games Workshop, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00149859

S. Gelly, J. B. Hoock, A. Rimmel, O. Teytaud, and Y. Kalemkarian, The parallelization of monte-carlo planning, Proceedings of the International Conference on Informatics in Control, Automation and Robotics, pp.198-203, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00287867

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

H. Kato and I. Takeuchi, Parallel Monte-Carlo Tree Search with Simulation Servers, 2010 International Conference on Technologies and Applications of Artificial Intelligence, 2008.
DOI : 10.1109/TAAI.2010.83

L. Kocsis and C. Szepesvari, Bandit Based Monte-Carlo Planning, ECML'06, pp.282-293, 2006.
DOI : 10.1007/11871842_29
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.1296

T. Lai and H. Robbins, Asymptotically efficient adaptive allocation rules, Advances in Applied Mathematics, vol.6, issue.1, pp.4-22, 1985.
DOI : 10.1016/0196-8858(85)90002-8

S. Tsai, T. Hsu, and . Hong, The computational intelligence of mogo revealed in taiwan's computer go tournaments, IEEE Transactions on Computational Intelligence and AI in Games, 2009.

V. Mnih, C. Szepesvári, and J. Audibert, Empirical Bernstein stopping, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.672-679, 2008.
DOI : 10.1145/1390156.1390241
URL : https://hal.archives-ouvertes.fr/hal-00834983

R. W. Schmittberger, New Rules for Classic Games, 1992.

Y. Wang, J. Audibert, and R. Munos, Algorithms for infinitely many-armed bandits, Advances in Neural Information Processing Systems, 2008.

Y. Wang and S. Gelly, Modifications of UCT and sequence-like simulations for Monte-Carlo Go, 2007 IEEE Symposium on Computational Intelligence and Games, pp.175-182, 2007.
DOI : 10.1109/CIG.2007.368095