The anatomy of easy problems: a constraint-satisfaction formulation

Authors:
Rina Dechter;Judea Pearl
Affiliations:
Computer Science Department, University of California, Los Angeles;Computer Science Department, University of California, Los Angeles
Venue:
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Year:
1985

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Abstract

This work aims towards the automatic generation of advice to guide the solution of difficult constraint-satisfaction problems (CSPs). The advice is generated by consulting relaxed, easy models which are backtrack-free. We identify a subset of CSPs whose syntactic and semantic properties make them easy to solve. The syntactic properties involve the structure of the constraint graph, while the semantic properties guarantee some local consistencies among the constraints. In particular, problems supported by tree-like constraint graphs, and some width-2 graphs, can be easily solved and are therefore chosen as the target model for the relaxation scheme. Optimal algorithms for solving easy problems are presented and analyzed. Finally, an efficient method is introduced for extracting advice from easy problems and using it to speedup the solution of hard problems.