Hnich B.Rossi R.Tarim S.A.Prestwich S.2023-06-162023-06-162009364204243097836420424300302-9743https://doi.org/10.1007/978-3-642-04244-7_36https://hdl.handle.net/20.500.14365/3399Association for Constraint Programming (ACP);Natl. Inf. Commun. Technol. Australia NICTA;Foundation for Science and Technology (FCT);Centre for Artificial Intelligence (CENTRIA);Portuguese Association for Artificial Intelligence (APPIA)15th International Conference on Principles and Practice of Constraint Programming, CP 2009 -- 20 September 2009 through 24 September 2009 -- Lisbon -- 77835Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. The only solution approach to date compiles down SCSPs into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This paper tries to overcome some of these drawbacks by automatically synthesizing filtering algorithms for global chance-constraints. These filtering algorithms are parameterized by propagators for the deterministic version of the chance-constraints. This approach allows the reuse of existing propagators in current constraint solvers and it enhances constraint propagation. Experiments show the benefits of this novel approach. © 2009 Springer Berlin Heidelberg.eninfo:eu-repo/semantics/openAccessConstraint propagationConstraint solversFiltering algorithmModeling frameworksParameterizedSolution approachSpace requirementsStochastic constraintsComputer programmingConstraint theorySignal filtering and predictionConference Object10.1007/978-3-642-04244-7_362-s2.0-70350413804