Decomposition of the Factor Encoding for CSPs
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 353-358.
Abstract
Generalized arc consistency (GAC) is one of the most fundamental properties for reducing the search space when solving constraint satisfaction problems (CSPs). Consistencies stronger than GAC have also been shown useful, but the challenge is to develop efficient and simple filtering algorithms. Several CSP transformations are proposed recently so that the GAC algorithms can be applied on the transformedCSP to enforce stronger consistencies. Among them, the factor encoding (FE) is shown to be promising with respect to recent higher-order consistency algorithms. Nonetheless, one potential drawback of the FE is the fact that it enlarges the table relations as it increases constraint arity. We propose a variation of the FE that aims at reducing redundant columns in the constraints of the FE while still preserving full pairwise consistency. Experiments show that the new approach is competitive over a variety of random and structured benchmarks.
@inproceedings{ijcai2015p56,
title = {Decomposition of the Factor Encoding for CSPs},
author = {Likitvivatanavong, Chavalit and Xia, Wei and Yap, Roland H.C.},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {353--358},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/56},
}
TY - CPAPER TI - Decomposition of the Factor Encoding for CSPs AU - Likitvivatanavong, Chavalit AU - Xia, Wei AU - Yap, Roland H.C. T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 353 EP - 358 UR - https://ijcai.org/proceedings/2015/56 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: