Towards Stochastic Constraint Programming: A Study of Online Multi-choice Knapsack with Deadlines
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Algorithms for stochastic CSPs
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Finding (α, ϑ)-solutions via sampled SCSPs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Filtering algorithms for global chance constraints
Artificial Intelligence
Flow-Based combinatorial chance constraints
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. The only solution approach to date compiles down SCSPs into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This paper tries to overcome some of these drawbacks by automatically synthesizing filtering algorithms for global chance-constraints. These filtering algorithms are parameterized by propagators for the deterministic version of the chance-constraints. This approach allows the reuse of existing propagators in current constraint solvers and it enhances constraint propagation. Experiments show the benefits of this novel approach.