High level path planning with uncertainty
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Valuation-based systems for Bayesian decision analysis
Operations Research
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Partial constraint satisfaction
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Extracting constraint satisfaction subproblems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Uncertainty in bipolar preference problems
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Event-Driven probabilistic constraint programming
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Filtering algorithms for global chance constraints
Artificial Intelligence
Flow-Based combinatorial chance constraints
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A generalization of SAT and #SAT for robust policy evaluation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The Constraint Satisfaction Problem (CSP) framework offers a simple and sound basis for representing and solving simple decision problems, without uncertainty. This paper is devoted to an extension of the CSP framework enabling us to deal with some decisions problems under uncertainty. This extension relies on a differentiation between the agent-controllable decision variables and the uncontrollable parameters whose values depend on the occurrence of uncertain events. The uncertainty on the values of the parameters is assumed to be given under the form of a probability distribution. Two algorithms are given, for computing respectively decisions solving the problem with a maximal probability, and conditional decisions mapping the largest possible amount of possible cases to actual decisions.