The Adaptive Constraint Engine
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Constraint Satisfaction Problems: Backtrack Search Revisited
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Domain filtering consistencies
Journal of Artificial Intelligence Research
Probabilistic consistency boosts MAC and SAC
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Sampling strategies and variable selection in weighted degree heuristics
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Learning How to Propagate Using Random Probing
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Integrating strong local consistencies into constraint solvers
CSCLP'09 Proceedings of the 14th Annual ERCIM international conference on Constraint solving and constraint logic programming
A framework for decision-based consistencies
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Constraint satisfaction problems: convexity makes all different constraints tractable
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Predicting good propagation methods for constraint satisfaction
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Constraint satisfaction problems: Convexity makes AllDifferent constraints tractable
Theoretical Computer Science
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Building adaptive constraint solvers is a major challenge in constraint programming. An important line of research towards this goal is concerned with ways to dynamically adapt the level of local consistency applied during search. A related problem that is receiving a lot of attention is the design of adaptive branching heuristics. The recently proposed adaptive variable ordering heuristics of Boussemart et al. use information derived from domain wipeouts to identify highly active constraints and focus search on hard parts of the problem resulting in important saves in search effort. In this paper we show how information about domain wipeouts and value deletions gathered during search can be exploited, not only to perform variable selection, but also to dynamically adapt the level of constraint propagation achieved on the constraints of the problem. First we demonstrate that when an adaptive heuristic is used, value deletions and domain wipeouts caused by individual constraints largely occur in clusters of consecutive or nearby constraint revisions. Based on this observation, we develop a number of simple heuristics that allow us to dynamically switch between enforcing a weak, and cheap local consistency, and a strong but more expensive one, depending on the activity of individual constraints. As a case study we experiment with binary problems using AC as the weak consistency and maxRPC as the strong one. Results from various domains demonstrate the usefulness of the proposed heuristics.