M. Araya-lópez, V. Thomas, O. Buffet, and F. Charpillet, A Closer Look at MOMDPs, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, p.ICTAI, 2010.
DOI : 10.1109/ICTAI.2010.101

K. Aström, Optimal control of Markov processes with incomplete state information, Journal of Mathematical Analysis and Applications, vol.10, issue.1, pp.174-205, 1965.
DOI : 10.1016/0022-247X(65)90154-X

A. Cassandra, M. Littman, and N. Zhang, Incremental Pruning: A simple, fast, exact method for Partially Observable Markov Decision Processes, pp.54-61, 1997.

I. Chadès, J. Carwardine, T. G. Martin, S. Nicol, R. Sabbadin et al., MOMDPs: A solution for modelling adaptive management problems, p.AAAI, 2012.

S. Choi, Reinforcement learning in nonstationary environments, 2000.

S. Choi, D. Yeung, and N. Zhang, An environment model for nonstationary reinforcement learning, pp.981-993, 2000.

S. Choi, N. Zhang, and D. Yeung, Solving Hidden-Mode Markov Decision Problems, In: AISTATS. pp, pp.19-26, 2001.

K. Doya, K. Samejima, K. Katagiri, and M. Kawato, Multiple Model-Based Reinforcement Learning, Neural Computation, vol.3, issue.6, pp.1347-1369, 2002.
DOI : 10.1016/S1364-6613(98)01221-2

L. Kaelbling, M. Littman, and A. Cassandra, Planning and acting in partially observable stochastic domains, Artificial Intelligence, vol.101, issue.1-2, pp.99-134, 1998.
DOI : 10.1016/S0004-3702(98)00023-X

L. Kocsis and C. Szepesvári, Bandit Based Monte-Carlo Planning, European Conference on Machine Learning, 2006.
DOI : 10.1007/11871842_29

S. Ong, S. Png, D. Hsu, and W. Lee, POMDPs for robotic tasks with mixed observability, Robotics: Science & Syst, 2009.

C. Papadimitriou and J. Tsitsiklis, The Complexity of Markov Decision Processes, Mathematics of Operations Research, vol.12, issue.3, pp.441-450, 1987.
DOI : 10.1287/moor.12.3.441

M. Puterman, Markov Decision Processes: Discrete dynamic stochastic programming, 1994.
DOI : 10.1002/9780470316887

B. Da-silva, E. Basso, A. Bazzan, and P. Engel, Dealing with non-stationary environments using context detection, Proceedings of the 23rd international conference on Machine learning , ICML '06, p.ICML, 2006.
DOI : 10.1145/1143844.1143872

D. Silver and J. Veness, Monte-Carlo planning in large POMDPs, pp.2164-2172, 2010.

S. Yu, Hidden semi-Markov models, Artificial Intelligence, vol.174, issue.2, pp.215-243, 2010.
DOI : 10.1016/j.artint.2009.11.011