Synthesizing Filtering Algorithms for Global Chance-Constraints

  • Brahim Hnich
  • Roberto Rossi
  • S. Armagan Tarim
  • Steven Prestwich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5732)


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.


Decision Variable Policy Tree Constraint Satisfaction Problem Stochastic Variable Constraint Propagation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Brahim Hnich
    • 1
  • Roberto Rossi
    • 2
  • S. Armagan Tarim
    • 3
  • Steven Prestwich
    • 4
  1. 1.Faculty of Computer ScienceIzmir University of EconomicsTurkey
  2. 2.Logistics, Decision and Information SciencesWageningen URThe Netherlands
  3. 3.Operations Management DivisionNottingham University Business SchoolUK
  4. 4.Cork Constraint Computation CentreUniversity College CorkIreland

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