R. Smallwood and E. Sondik, The Optimal Control of Partially Observable Markov Processes over a Finite Horizon, Operations Research, vol.21, issue.5, pp.1071-1088, 1973.
DOI : 10.1287/opre.21.5.1071

S. Thrun, Probabilistic algorithms in robotics. AI Magazine, pp.93-109, 2000.

L. Mihaylova, T. Lefebvre, H. Bruyninckx, K. Gadeyne, and J. Schutter, Active sensing for robotics -a survey, Proc. 5th Intl. Conf. On Numerical Methods and Applications, 2002.

S. Ji and L. Carin, Cost-sensitive feature acquisition and classification, Pattern Recognition, vol.40, issue.5, pp.1474-1485, 2007.
DOI : 10.1016/j.patcog.2006.11.008

A. Hero, D. Castan, D. Cochran, and K. Kastella, Foundations and Applications of Sensor Management, 2007.
DOI : 10.1007/978-0-387-49819-5

A. Cassandra, Exact and approximate algorithms for partially observable Markov decision processes, 1998.

R. Bellman, The theory of dynamic programming, Bulletin of the American Mathematical Society, vol.60, issue.6, pp.503-516, 1954.
DOI : 10.1090/S0002-9904-1954-09848-8

G. Monahan, State of the Art???A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms, Management Science, vol.28, issue.1, pp.1-16, 1982.
DOI : 10.1287/mnsc.28.1.1

M. Hauskrecht, Value-function approximations for partially observable Markov decision processes, Journal of Artificial Intelligence Research, vol.13, pp.33-94

W. Lovejoy, Computationally Feasible Bounds for Partially Observed Markov Decision Processes, Operations Research, vol.39, issue.1, pp.162-175
DOI : 10.1287/opre.39.1.162

J. Pineau, G. Gordon, and S. Thrun, Anytime point-based approximations for large POMDPs, Journal of Artificial Intelligence Research (JAIR), vol.27, pp.335-380, 2006.

M. Spaan and N. Vlassis, Perseus: Randomized point-based value iteration for POMDPs, Journal of Artificial Intelligence Research, vol.24, pp.195-220, 2005.

T. Smith and R. Simmons, Point-based POMDP algorithms: Improved analysis and implementation, Proc. of the Int. Conf. on Uncertainty in Artificial Intelligence (UAI), 2005.

H. Kurniawati, D. Hsu, and W. Lee, SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces, Robotics: Science and Systems IV, 2008.
DOI : 10.15607/RSS.2008.IV.009
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.450

R. Kaplow, Point-based POMDP solvers: Survey and comparative analysis, 2010.

T. Cover and J. Thomas, Elements of Information Theory, 1991.

M. Araya-lópez, O. Buffet, V. Thomas, and F. Charpillet, A POMDP extension with beliefdependent rewards ? extended version, 2010.

R. Saigal, On Piecewise Linear Approximations to Smooth Mappings, Mathematics of Operations Research, vol.4, issue.2, pp.153-161, 1979.
DOI : 10.1287/moor.4.2.153

M. Spaan, Cooperative active perception using POMDPs, AAAI 2008 Workshop on Advancements in POMDP Solvers, 2008.

S. Ross, J. Pineau, S. Paquet, and B. , Chaib-draa. Online planning algorithms for POMDPs, Journal of Artificial Intelligence Research (JAIR), vol.32, pp.663-704, 2008.