A parameterized local consistency for redundant modeling in weighted CSPs
Refereed conference paper presented and published in conference proceedings


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AbstractThe weighted constraint satisfaction problem (WCSP) framework is a soft constraint framework which can model many real life optimization or over-constrained problems. While there are many local consistency notions available to speed up WCSP solving, in this paper, we investigate how to effectively combine and channel mutually redundant WCSP models to increase constraint propagation. This successful technique for reducing search space in classical constraint satisfaction has been shown non-trivial when adapted for the WCSP framework. We propose a parameterized local consistency LB(m,Phi), which can be instantiated with any local consistency P for single models and applied to a combined model with m sub-models, and also provide a simple algorithm to enforce it. We instantiate LB(2,Phi) with different state-of-the-art local consistencies AC*, FDAC*, and EDAC*, and demonstrate empirically the efficiency of the algorithm using different benchmark problems.
All Author(s) ListLaw YC, Lee JHM, Woo MHC
Name of Conference20th Australian Joint Conference on Artificial Intelligence
Start Date of Conference02/12/2007
End Date of Conference06/12/2007
Place of ConferenceGold Coast
Country/Region of ConferenceAustralia
Journal nameLecture Notes in Artificial Intelligence
Year2007
Month1
Day1
Volume Number4830
PublisherSPRINGER-VERLAG BERLIN
Pages191 - 201
ISBN978-3-540-76926-2
ISSN0302-9743
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-20-05 at 00:36