Radio Link Frequency Assignment
Constraints
Contradicting Conventional Wisdom in Constraint Satisfaction
PPCP '94 Proceedings of the Second International Workshop on Principles and Practice of Constraint Programming
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
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Domain filtering consistencies for non-binary constraints
Artificial Intelligence
Experimental studies of variable selection strategies based on constraint weights
Journal of Algorithms
Efficient constraint propagation engines
ACM Transactions on Programming Languages and Systems (TOPLAS)
Heuristics for Dynamically Adapting Propagation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on 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
Fast forward planning by guided enforced hill climbing
Engineering Applications of Artificial Intelligence
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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 propagation method applied on the constraints of the problem during search. In this paper we present a heuristic approach to this problem based on the monitoring of propagation events like value deletions and domain wipeouts. We develop a number of heuristics that allow the constraint solver to dynamically switch between a weaker and cheap local consistency and a stronger, but more expensive one, when certain conditions are met. The success of this approach is based on the observation that propagation events for individual constraints in structured problems mostly occur in clusters of nearby revisions. Hence, parts of the search space where certain constraints are highly active can be identified and exploited paving the way for the informed use of constraint propagation techniques. In this paper we first give some experimental results displaying the clustering of propagation events in structured binary CSPs. Then we present simple heuristics that exploit this clustering to efficiently switch between different local consistencies on individual constraints during search. Finally, we make an experimental study on various binary CSPs demonstrating the effectiveness of the proposed heuristics.