Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
The logical view of conditioning and its application to possibility and evidence theories
International Journal of Approximate Reasoning
A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Approximating optimal policies for partially observable stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Possibility theory as a basis for qualitative decision theory
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A heuristic variable grid solution method for POMDPs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Incremental methods for computing bounds in partially observable Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Qualitative decision theory with Sugeno integrals
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Qualitative reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Fuzzy optimality relation for perceptive MDPs---the average case
Fuzzy Sets and Systems
Decision with uncertainties, feasibilities, and utilities: towards a unified algebraic framework
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
An algebraic graphical model for decision with uncertainties, feasibilities, and utilities
Journal of Artificial Intelligence Research
Algebraic Markov decision processes
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Necessity-based Choquet integrals for sequential decision making under uncertainty
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Qualitative MDPs and POMDPs: an order-of-magnitude approximation
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Decomposition of multi-operator queries on semiring-based graphical models
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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In this article we propose a qualitative (ordinal) counterpart for the Partially Observable Markov Decision Processes model (POMDP) in which the uncertainty, as well as the preferences of the agent, are modeled by possibility distributions. This qualitative counterpart of the POMDP model relies on a possibilistic theory of decision under uncertainty, recently developed. One advantage of such a qualitative framework is its ability to escape from the classical obstacle of stochastic POMDPs, in which even with a finite state space, the obtained belief state space of the POMDP is infinite. Instead, in the possibilistic framework even if exponentially larger than the state space, the belief state space remains finite.