Domain filtering consistencies for non-binary constraints

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. ...

• Type: Journal
• Year: 2008
• Where: AI
• Authors: Christian Bessiere, Kostas Stergiou, Toby Walsh
• Keywords: AI 2008, Arc Consistency, Artificial Intelligence, Consistency, Domain Filtering Consistencies