Dual viewpoint heuristics for binary constraint satisfaction problems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Dual Models of Permutation Problems
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Breaking Row and Column Symmetries in Matrix Models
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Indexical-Based Solver Learning
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Model induction: a new source of CSP model redundancy
Eighteenth national conference on Artificial intelligence
Breaking value symmetries in matrix models using channeling constraints
Proceedings of the 2005 ACM symposium on Applied computing
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Solving Non-Boolean Satisfiability Problems with Stochastic Local Search: A Comparison of Encodings
Journal of Automated Reasoning
Removing propagation redundant constraints in redundant modeling
ACM Transactions on Computational Logic (TOCL)
Dual modelling of permutation and injection problems
Journal of Artificial Intelligence Research
The design of ESSENCE: a constraint language for specifying combinatorial problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficient representation of adhoc constraints
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Propagation redundancy for permutation channels
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The rules of constraint modelling
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Mapping problems with finite-domain variables to problems with boolean variables
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
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If we have two representations of a problem as constraint satisfaction problem (CSP) models, it has been shown that combining the models using channeling constraints can increase constraint propagation in tree search CSP solvers. Handcrafting two CSP models for a problem, however, is often time-consuming. In this paper, we propose model induction, a process which generates a second CSP model from an existing model using channeling constraints, and study its theoretical properties. The generated induced model is in a different viewpoint, i.e., set of variables. It is mutually redundant to and can be combined with the input model, so that the combined model contains more redundant information, which is useful to increase constraint propagation. We also propose two methods of combining CSP models, namely model intersection and model channeling. The two methods allow combining two mutually redundant models in the same and different viewpoints respectively. We exploit the applications of model induction, intersection, and channeling and identify three new classes of combined models, which contain different amounts of redundant information. We construct combined models of permutation CSPs and show in extensive benchmark results that the combined models are more robust and efficient to solve than the single models.