Compiling constraint satisfaction problems
Artificial Intelligence
Dynamic Bundling: Less Effort for More Solutions
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Maintaining Generalized Arc Consistency on Ad Hoc r-Ary Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Neighborhood interchangeability and dynamic bundling for non-binary finite CSPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Exploiting interchangeabilities in constraint satisfaction problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Generalized arc consistency for positive table constraints
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Many-to-many interchangeable sets of values in CSPs
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Eliminating redundancy in CSPs through merging and subsumption of domain values
ACM SIGAPP Applied Computing Review
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The concept of Interchangeability was developed to deal with redundancy of values in the same domain. Conventional algorithms for detecting Neighborhood Interchangeability work by gradually establishing relationships between values from scratch. We propose the opposite strategy: start by assuming everything is interchangeable and disprove certain relations as more information arises. Our refutation-based algorithms have much better lower bounds whereas the lower bound and the upper bound of the traditional algorithms are asymptotically identical.