A parameterized local consistency for redundant modeling in weighted CSPs

Authors:
Y. C. Law;J. H. M. Lee;M. H. C. Woo
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

Citing 7
Cited 2

Increasing Constraint Propagation by Redundant Modeling: \an Experience Report

Constraints
Node and arc consistency in weighted CSP

Eighteenth national conference on Artificial intelligence
Solving weighted CSP by maintaining arc consistency

Artificial Intelligence
Valued constraint satisfaction problems: hard and easy problems

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
In the quest of the best form of local consistency for weighted CSP

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Existential arc consistency: getting closer to full arc consistency in weighted CSPs

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Speeding up weighted constraint satisfaction using redundant modeling

AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence

Redundant modeling in permutation weighted constraint satisfaction problems

Constraints
Local arc consistency for non-invertible semirings, with an application to multi-objective optimization

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The 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, φ), which can be instantiated with any local consistency φ 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, φ) with different state-of-the-art local consistencies AC*, FDAC*, and EDAC*, and demonstrate empirically the efficiency of the algorithm using different benchmark problems.