Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Constraint Satisfaction Problems: Backtrack Search Revisited
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
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
Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
Probabilistic consistency boosts MAC and SAC
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incomplete tree search using adaptive probing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Sampling strategies and variable selection in weighted degree heuristics
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
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In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usually only apply a standard method, typically (generalized) arc consistency, on all constraints throughout search. Advanced solvers additionally allow for the modeler to choose among an array of propagators for certain (global) constraints. Since complex interactions exist among constraints, deciding in the modelling phase which propagation method to use on given constraints can be a hard task that ideally we would like to free the user from. In this paper we propose a simple technique towards the automation of this task. Our approach exploits information gathered from a random probing preprocessing phase to automatically decide on the propagation method to be used on each constraint. As we demonstrate, data gathered though probing allows for the solver to accurately differentiate between constraints that offer little pruning as opposed to ones that achieve many domain reductions, and also to detect constraints and variables that are amenable to certain propagation methods. Experimental results from an initial evaluation of the proposed method on binary CSPs demonstrate the benefits of our approach.