Stochastic Constraint Programming

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dc.creatorWalsh, Toby
dc.date2009-03-06
dc.date.accessioned2026-07-07T12:49:50Z
dc.date.available2026-07-07T12:49:50Z
dc.descriptionTo model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochastic constraint programs, and propose a number of complete algorithms and approximation procedures. Finally, we discuss a number of extensions of stochastic constraint programming to relax various assumptions like the independence between stochastic variables, and compare with other approaches for decision making under uncertainty.
dc.descriptionProceedings of the 15th Eureopean Conference on Artificial Intelligence
dc.identifierhttps://arxiv.org/abs/0903.1152
dc.identifierhttp://arxiv.org/abs/0903.1152
dc.identifierECAI 2002: 111-115
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/222506
dc.subjectArtificial Intelligence
dc.subjectI.2.4
dc.titleStochastic Constraint Programming
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