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To model combinatorial decision problems involving uncertainty and probability, the authors introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which they 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. They give a semantics for stochastic constraint programs, and propose a number of complete algorithms and approximation procedures. Finally, they 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.
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