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Synthesizing Filtering Algorithms for Global Chance-Constraints

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

9 Alıntılar (Scopus)

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıPrinciples and Practice of Constraint Programming - CP 2009 - 15th International Conference, CP 2009, Proceedings
Sayfalar439-453
Sayfa sayısı15
DOI'lar
Yayın durumuYayınlandı - 2009
Harici olarak yayınlandıEvet
Etkinlik15th International Conference on Principles and Practice of Constraint Programming, CP 2009 - Lisbon, !!Portugal
Süre: 20 Eyl 200924 Eyl 2009

Yayın serisi

AdıLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim5732 LNCS
ISSN (Basılı)0302-9743
ISSN (Elektronik)1611-3349

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???event.eventtypes.event.conference???15th International Conference on Principles and Practice of Constraint Programming, CP 2009
Ülke/Bölge!!Portugal
ŞehirLisbon
Periyot20/09/0924/09/09

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