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Synthesizing Filtering Algorithms for Global Chance-Constraints

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming - CP 2009 - 15th International Conference, CP 2009, Proceedings
Pages439-453
Number of pages15
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event15th International Conference on Principles and Practice of Constraint Programming, CP 2009 - Lisbon, Portugal
Duration: 20 Sept 200924 Sept 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5732 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Principles and Practice of Constraint Programming, CP 2009
Country/TerritoryPortugal
CityLisbon
Period20/09/0924/09/09

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