Scenario-based stochastic constraint programming

S. Manandhar, A. Tarim, T. Walsh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new (but equivalent) semantics based on scenarios. Using this semantics, we can compile stochastic constraint programs down into conventional (nonstochastic) constraint programs. This allows us to exploit the full power of existing constraint solvers. We have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modelling language [Hentenryck et al., 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as finance, agriculture and production.
Original languageEnglish
Title of host publicationProceedings of the 18th International Joint Conference in Artificial Intelligence
Number of pages5
Publication statusPublished - 2003
EventIJCAI 03 - Acapulco, Mexico
Duration: 9 Aug 200315 Aug 2003


ConferenceIJCAI 03

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