Lazy Arc Consistency

No. 1: Agents, AI in Art and Entertainment, Knowledge Representation, and Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Data Consistency

Arc consistency filtering is widely used in the framework of binary constraint satisfaction problems: with a low complexity, inconsistency may be detected,and domains are filtered. In this paper, we show that when detecting inconsistency is the objective, a systematic domain filtering is useless and a lazy approach is more adequate. Whereas usual arc consistency algorithms produce the maximum arc consistent sub-domain, when it exists, we propose a method, called LACY, which only looks for any arc consistent sub-domain. The algorithm is then extended to provide the additional service of locating one variable with a minimum domain cardinality in the maximum arc consistent sub-domain, without necessarily computing all domain sizes. Finally, we compare traditional AC enforcing and lazy AC enforcing using several benchmark problems, both randomly generated CSP and real life problems.