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EMO
2009
Springer

Constraint Programming

9 years 4 hour ago
Constraint Programming
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.
Pascal Van Hentenryck
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where EMO
Authors Pascal Van Hentenryck
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