Constraint propagation with interval labels
Artificial Intelligence
Planning as search: a quantitative approach
Artificial Intelligence
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Tree search and ARC consistency in constraint satisfaction algorithms
Search in Artificial Intelligence
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Dynamic Bundling: Less Effort for More Solutions
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
AbsCon: A Prototype to Solve CSPs with Abstraction
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Exploiting functional dependencies in declarative problem specifications
Artificial Intelligence
Introduction to the Special Volume on Reformulation
Artificial Intelligence - Special volume on reformulation
Reformulating constraint satisfaction problems to improve scalability
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Integrating heuristics for constraint satisfaction problems: a case study
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Exploiting symmetry in lifted CSPs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Abstraction techniques are important for solving constraint satisfaction problems with global constraints and low solution density. In the presence of global constraints, backtracking search is unable to prune partial solutions. It therefore operates like pure generate-and-test. Abstraction improves on generate-and-test by enabling entire subsets of the solution space to be pruned early in a backtracking search process. This paper describes how abstraction spaces can be characterized in terms of approximate symmetries of the original, concrete search space. It defines two special types of approximate symmetry, called "range symmetry" and "domain symmetry", which apply to function finding problems. It also presents algorithms for automatically synthesizing hierarchic problem solvers based on range or domain symmetry. The algorithms operate by analyzing declarative descriptions of classes of constraint satisfaction problems. Both algorithms have been fully implemented. This paper concludes by presenting data from experiments testing the two synthesis algorithms and the resulting problem solvers on NP-hard scheduling and partitioning problems.