Heuristic Planning with SAT: Beyond Uninformed Depth-First Search
Planning-specific heuristics for SAT have recently been shown to produce planners that match best earlier ones that use other search methods, including the until now dominant heuristic state-space search. The heuristics are simple and natural, and enforce pure depth-first search with backward chaining in the standard conflict-directed clause learning (CDCL) framework. In this work we consider alternatives to pure depth-first search, and show that carefully chosen randomized search order, which...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the Australasian Joint Conference on Artificial Intelligence (AI 2010)|
|01_Rintanen_Heuristic_Planning_with_SAT:_2010.pdf||185.51 kB||Adobe PDF||Request a copy|
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