Arc-Consistency in Dynamic CSPs Is No More Prohibitive

Constraint satisfaction problems CSPs are widely used in Arti cial Intelligence The problem of the exist ence of a solution in a CSP being NP complete ltering techniques and particularly arc consistency are essen tial They remove some local inconsistencies and so make the search easier Since many problems in AI require a dynamic environment the model was exten ded to dynamic CSPs DCSPs and some incremental arc consistency algorithms were proposed However all of them have important drawbacks DnAC has an expensive worst case space complexity and a bad average time complexity ACjDC has a non optimal worst case time complexity which prevents from taking advantage of its good space complexity The algorithm we present in this paper has both lower space requirements and better time performances than DnAC while keeping an optimal worst case time complexity