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Stochastic Constraint Programming

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Executive Summary

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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