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To model combinatorial decision problems involving uncertainty and probability, the authors 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). They also provide a new (but equivalent) semantics based on scenarios. Using this semantics, they can compile stochastic constraint programs down into conventional (nonstochastic) constraint programs. This allows one to exploit the full power of existing constraint solvers. They have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modeling language [Hentenryck et al., 1999]. To illustrate the potential of this framework, they model a wide range of problems in areas as diverse as finance, agriculture and production.
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