Prestwich S.Tarim S.A.Rossi R.Hnich B.2023-06-162023-06-162009364204243097836420424300302-9743https://doi.org/10.1007/978-3-642-04244-7_53https://hdl.handle.net/20.500.14365/3400Association 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 Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several solution methods have been proposed but none seems practical for large multi-stage problems. We propose an incomplete approach: specifying a policy tree indirectly by a parameterised function, whose parameter values are found by evolutionary search. On some problems this method is orders of magnitude faster than a state-of-the-art scenario-based approach, and it also provides a very compact representation of policy trees. © 2009 Springer Berlin Heidelberg.eninfo:eu-repo/semantics/closedAccessCombinatorial problemCompact representationConstraint programmingDecision variablesEvolutionary searchMulti-stage problemOrders of magnitudeParameter valuesSolution methodsStochastic constraintsComputer programmingConstraint theoryEvolutionary algorithmsUnmanned aerial vehicles (UAV)Problem solvingConference Object10.1007/978-3-642-04244-7_532-s2.0-70350423545