Domain filtering consistencies for non-binary constraints

作     者：Bessiere, Christian Stergiou, Kostas Walsh, Toby 

作者机构：CNRS U LIRMM Montpellier France Univ New S Wales Natl ICT Australia & Sch Comp Sci & Engn Kensington NSW 2033 Australia 

出 版 物：《ARTIFICIAL INTELLIGENCE》 (人工智能)

年 卷 期：2008年第172卷第6-7期

页      面：800-822页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：constraint programming constraint satisfaction problems non-binary constraints local consistency inverse consistency pairwise consistency 

摘      要：In non-binary constraint satisfaction problems, the study of local consistencies that only prune values from domains has so far been largely limited to generalized arc consistency or weaker local consistency properties. This is in contrast with binary constraints where numerous such domain filtering consistencies have been proposed. In this paper we present a detailed theoretical, algorithmic and empirical study of domain filtering consistencies for non-binary problems. We study three domain filtering consistencies that are inspired by corresponding variable based domain filtering consistencies for binary problems. These consistencies are stronger than generalized arc consistency, but weaker than pairwise consistency, which is a strong consistency that removes tuples from constraint relations. Among other theoretical results, and contrary to expectations, we prove that these new consistencies do not reduce to the variable based definitions of their counterparts on binary constraints. We propose a number of algorithms to achieve the three consistencies. One of these algorithms has a time complexity comparable to that for generalized arc consistency despite performing more pruning. Experiments demonstrate that our new consistencies are promising as they can be more efficient than generalized arc consistency on certain non-binary problems. (c) 2007 Elsevier B.V. All rights reserved.