J. Baxter, A. Tridgell, and L. Weaver, Experiments in parameter learning using temporal, 1998.

M. Buro, From Simple Features to Sophisticated Evaluation Functions, 1st International Conference on Computers and Games, pp.126-145, 1999.
DOI : 10.1007/3-540-48957-6_8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.1627

R. Coulom, Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, 5th International Conference on Computer and Games, pp.2006-2011, 2006.
DOI : 10.1007/978-3-540-75538-8_7
URL : https://hal.archives-ouvertes.fr/inria-00116992

M. Enzenberger, Evaluation in Go by a Neural Network Using Soft Segmentation, 10th Advances in Computer Games Conference, pp.97-108, 2003.
DOI : 10.1007/978-0-387-35706-5_7

S. Gelly, Y. Wang, R. Munos, and O. Teytaud, Modification of UCT with patterns in Monte-Carlo Go INRIA, 2006.

L. Kocsis and C. Szepesvari, Bandit Based Monte-Carlo Planning, 15th European Conference 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

J. Schaeffer, M. Hlynka, and V. Jussila, Temporal difference learning applied to a high-performance game-playing program, 17th International Joint Conference on Artificial Intelligence, pp.529-534, 2001.

D. Silver, R. Sutton, and M. Müller, Reinforcement learning of local shape in the game of Go, 20th International Joint Conference on Artificial Intelligence, pp.1053-1058, 2007.

R. Sutton, Learning to predict by the methods of temporal differences, Machine Learning, pp.9-44, 1988.
DOI : 10.1007/BF00115009

R. Sutton, Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming, 7th International Conference on Machine Learning, pp.216-224, 1990.
DOI : 10.1016/B978-1-55860-141-3.50030-4

R. Sutton, Generalization in reinforcement learning: Successful examples using sparse coarse coding, Advances in Neural Information Processing Systems, pp.1038-1044, 1996.

R. Sutton and A. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

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