Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Decomposition of the Factor Encoding for CSPs / 353
Chavalit Likitvivatanavong, Wei Xia, Roland H. C. Yap

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.