Bessi~re, C., Arc-consistency and arc-consistency again (Research Note), Artificial Intelligence 65 (1994) 179-190. There is no need to show the importance of arc-consistency in constraint networks. Mohr and Henderson [9] have proposed AC-4, an algorithm with optimal worst-case time complexity. But it has two drawbacks: its space complexity and average time complexity. In problems with many solutions, where constraints are large, these drawbacks become so important that users often replace AC-4 by AC-3 [8], a non-optimal algorithm. In this paper, we propose a new algorithm, AC-6, which keeps the optimal worst-case time complexity of AC-4 while working out the drawback of space complexity. Moreover, the average time complexity of AC-6 is optimal for constraint networks where nothing is known about the constraint semantics.
1. Introduction
Constraint networks provide a useful way to formulate problems such as design, scene labeling, temporal reasoning, and more recently natural language parsing. The problem of the existence of solutions in a constraint network is NP-complete. Hence, consistency techniques have been widely studied to simplify constraint networks before or during the search for solutions. They