Stochastic Constraint Programming

Details Stochastic Constraint Programming Stochastic Constraint Programming

2009-03-06

arxiv.org/abs/0903.1152v1

ECAI 2002: 111-115

Computer Science

Artificial Intelligence

Proceedings of the 15th Eureopean Conference on Artificial Intelligence

Scientific paper

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