O. Lebeltel, P. Bessì-ere, J. Diard, and E. Mazer, Bayesian Robot Programming, Autonomous Robots, vol.16, issue.1, pp.49-79, 2004.
DOI : 10.1023/B:AURO.0000008671.38949.43

URL : https://hal.archives-ouvertes.fr/inria-00189723

S. Rabin, Implementing a state machine language, AI Game Programming Wisdom, pp.314-320, 2002.

M. Ponsen and I. P. Spronck, Improving adaptive game AI with evolutionary learning, pp.389-396, 2004.

M. Preuss, N. Beume, H. Danielsiek, T. Hein, B. Naujoks et al., Towards Intelligent Team Composition and Maneuvering in Real-Time Strategy Games, Transactions on Computational Intelligence and AI in Games (CIG), pp.82-98, 2010.
DOI : 10.1109/TCIAIG.2010.2047645

B. Marthi, S. Russell, D. Latham, and C. Guestrin, Concurrent hierarchical reinforcement learning, International Joint Conference of Artificial Intelligence, IJCAI, pp.779-785, 2005.

C. Madeira, V. Corruble, and G. Ramalho, Designing a reinforcement learning-based adaptive AI for large-scale strategy games, AI and Interactive Digital Entertainment Conference, AIIDE (AAAI), 2006.
URL : https://hal.archives-ouvertes.fr/hal-01351276

D. W. Aha, M. Molineaux, and M. J. Ponsen, Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game, ICCBR '05, pp.5-20, 2005.
DOI : 10.1007/11536406_4

M. Sharma, M. Holmes, J. Santamaria, A. Irani, C. L. Isbell et al., Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL, International Joint Conference of Artificial Intelligence , IJCAI, 2007.

M. Molineaux, D. W. Aha, and P. Moore, Learning continuous action models in a real-time strategy strategy environment, FLAIRS Conference, pp.257-262, 2008.

B. G. Weber, P. Mawhorter, M. Mateas, and A. Jhala, Reactive planning idioms for multi-scale game AI, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, 2010.
DOI : 10.1109/ITW.2010.5593363

R. Balla and A. Fern, Uct for tactical assault planning in realtime strategy games, International Joint Conference of Artificial Intelligence, IJCAI, pp.40-45, 2009.

J. Hagelbäck and S. J. Johansson, A Multiagent Potential Field-Based Bot for Real-Time Strategy Games, International Journal of Computer Games Technology, vol.5, issue.1, pp.1-410, 2009.
DOI : 10.1109/21.44033

S. Wintermute, J. Z. , J. Xu, and J. E. Laird, Sorts: A humanlevel approach to real-time strategy AI, AI and Interactive Digital Entertainment Conference, AIIDE (AAAI), pp.55-60, 2007.

M. Cutumisu and D. Szafron, An architecture for game behavior AI: Behavior multi-queues, AI and Interactive Digital Entertainment Conference, AIIDE (AAAI), 2009.

D. Isla, Handling complexity in the Halo 2 AI, Game Developers Conference, 2005.

R. S. Sutton, A. G. Barto, and R. Learning, An Introduction (Adaptive Computation and Machine Learning), 1998.

S. Ontañón, K. Mishra, N. Sugandh, and A. Ram, Case-Based Planning and Execution for Real-Time Strategy Games, ICCBR '07, pp.164-178, 2007.
DOI : 10.1007/978-3-540-74141-1_12

M. Chung, M. Buro, and J. Schaeffer, Monte carlo planning in rts games, IEEE Symposium on Computational Intelligence and Games (CIG), 2005.

J. Diard, P. Bessì, and E. Mazer, A survey of probabilistic models using the bayesian programming methodology as a unifying framework, Conference on Computational Intelligence, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00019254

D. J. Mackay, Information Theory, Inference, and Learning Algorithms, 2003.

M. J. Beal, Variational algorithms for approximate bayesian inference, 2003.

P. Bessì-ere, C. Laugier, and R. Siegwart, Probabilistic Reasoning and Decision Making in Sensory-Motor Systems, 2008.

F. Colas, J. Diard, and P. Bessire, Common Bayesian Models for Common Cognitive Issues, Acta Biotheoretica, vol.86, issue.3, pp.191-216, 2010.
DOI : 10.1007/s10441-010-9101-1

URL : https://hal.archives-ouvertes.fr/hal-00530356

R. , L. Hy, A. Arrigoni, P. Bessiere, and O. Lebeltel, Teaching bayesian behaviours to video game characters, Robotics and Autonomous Systems, vol.47, pp.177-185, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00182073

J. Asmuth, L. Li, M. Littman, A. Nouri, and D. Wingate, A bayesian sampling approach to exploration in reinforcement learning, Uncertainty in Artificial Intelligence, UAI, pp.19-26, 2009.