L. V. Allis, Searching for Solutions in games and Artificial Intelligence, Maastricht: Rrijksuniversiteit Limburg, 1995.

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

H. Avetisyan and R. J. Lorentz, Selective Search in an Amazons Program, Lecture Notes in Computer Science, vol.2883, pp.123-141, 2003.
DOI : 10.1007/978-3-540-40031-8_9

E. R. Berlekamp, Sums of N × 2 Amazons, Institute of Mathematical Statistics Lecture Notes -Monograph Series, vol.35, 2000.

M. Buno, Simple Amazons endgames and their connection to Hamilton circuits in cubic subgrid graphs. Computers and Games, Lecture Notes in Computer Science, vol.2063, pp.250-261, 2001.

R. Coulom, Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, Lecture Notes in Computer Science, vol.4630, pp.72-83, 2007.
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, ICGA Journal, vol.30, pp.198-208, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00149859

S. Gelly, Y. Wang, R. Munos, and O. Teytaud, Modification of UCT with patterns in Monte-Carlo Go, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00117266

P. Hensgens, A Knowledge-based Approach of the Game of Amazons, 2001.

K. Hoki and M. Muramatsu, Efficiency of three forward-pruning techniques in shogi: Futility pruning, null-move pruning, and Late Move Reduction (LMR), Entertainment Computing, vol.3, issue.3, pp.51-57, 2012.
DOI : 10.1016/j.entcom.2011.11.003

T. Kaneko, Evaluation Functions of Computer Shogi Programs and Supervised Learning Using Game Records, Journal of Japanese Society for Artificial Intelligence, vol.27, pp.75-82, 2012.

J. Kloetzer, H. Iida, and B. Bouzy, The Monte-Carlo Approach in Amazons, Computer Games Workshop, pp.185-192, 2007.

J. Kloetzer, H. Iida, and B. Bouzy, A comparative study of solvers in Amazons endgames, 2008 IEEE Symposium On Computational Intelligence and Games, 2008.
DOI : 10.1109/CIG.2008.5035665

J. Kloetzer, H. Iida, and B. Bouzy, Playing Amazons Endgames1, ICGA Journal, vol.32, issue.3, pp.140-148, 2009.
DOI : 10.3233/ICG-2009-32303

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.211.9926

J. Kloetzer, Experiments in Monte-Carlo Amazons, IPSJ SIG Technical Report, vol.2010, issue.6, pp.1-4, 2010.

J. Kloetzer, Monte-Carlo Opening Books for Amazons, 7th conference on Computer and Games, 2010.
DOI : 10.1007/978-3-642-17928-0_12

J. Kloetzer, Monte-Carlo Techniques: Application to Monte Carlo tree search and Amazon, 2011.

D. Knuth and R. Moore, An analysis of alpha-beta pruning, Artificial Intelligence, vol.6, issue.4, pp.293-326, 1975.
DOI : 10.1016/0004-3702(75)90019-3

L. Kocsis and C. Szepesvári, Bandit Based Monte-Carlo Planning, 17th European Conf. on Machine Learning, pp.282-293, 2006.
DOI : 10.1007/11871842_29

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.1296

T. Kozelek, Methods of MCTS and the game Arimaa, 2009.

J. Lieberum, An evaluation function for the game of amazons, Theoretical Computer Science, vol.349, issue.2, pp.230-244, 2005.
DOI : 10.1016/j.tcs.2005.09.048

H. Matsubara, H. Iida, and R. Grimbergen, Chess, Shogi, Go, natural developments in game research, ICCA Journal, vol.19, pp.103-112, 1996.

R. Motwani and P. Raghavan, Randomized Algorithms, 1995.
DOI : 10.1145/234313.234327