Arc and path consistence revisited
Artificial Intelligence
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Synthesizing constraint expressions
Communications of the ACM
Asynchronous aggregation and consistency in distributed constraint satisfaction
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Maintaining Arc Consistency in Non-Binary Dynamic CSPs using Simple Tabular Reduction
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
CSCLP'09 Proceedings of the 14th Annual ERCIM international conference on Constraint solving and constraint logic programming
Interactively solving school timetabling problems using extensions of constraint programming
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
Dynamic SAT with decision change costs: formalization and solutions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Dynamic virtual arc consistency
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Constraint satisfaction problems (CSPs) provide a model often used in Artificial Intelligence. Since the problem of the existence of a solution in a CSP is an NP-complete task, many filtering techniques have been developed for CSPs. The most used filtering techniques are those achieving arc-consistency. Nevertheless, many reasoning problems in AI need to be expressed in a dynamic environment and almost all the techniques already developed to solve CSPs deal only with static CSPs. So, in this paper, we first define what we call a dynamic CSP, and then, give an algorithm achieving arc-consistency in a dynamic CSP. The performances of the algorithm proposed here and of the best algorithm achieving arc-consistency in static CSPs are compared on randomly generated dynamic CSPs. The results show there is an advantage to use our specific algorithm for dynamic CSPs in almost all the cases tested.