The hazards of fancy backtracking
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Journal of Artificial Intelligence Research
Local search with constraint propagation and conflict-based heuristics
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Maintaining Arc-Consistency within Dynamic Backtracking
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An explanation-based tools for debugging constraint satisfaction problems
Applied Soft Computing
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A search-infer-and-relax framework for integrating solution methods
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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Recently, two new backtracking algorithms, dynamic backtracking (DB) and partial order dynamic backtracking (PDB) have been presented. These algorithms have the property to be additive on disjoint subproblems and yet use only polynomial space. Unlike DB, PDB only imposes a partial search order and therefore appears to have more freedom than DB to explore the search space. However, both algorithms are not directly comparable in terms of flexibility. In this paper we present new backtracking algorithms that are obtained by relaxing the ordering conditions of PDB. This gives them additional flexibility while still being additive on disjoint subproblems. In particular, we show that our algorithms generalize both DB and PDB.