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- W58147275 abstract "Logic, and declarative representation of knowledge in general, have long been a preferred framework for problem solving in AI. However, specific subareas of AI have been eager to abandon general-purpose knowledge representation in favor of methods that seem to address their computational core problems better. In planning, for example, state-space search has in the last several years been preferred to logic-based methods such as SAT. In our recent work, we have demonstrated that the observed performance differences between SAT and specialized state-space search methods largely go back to the difference between a blind (or at least planning-agnostic) and a planning-specific search method. If SAT search methods are given even simple heuristics which make the search goal-directed, the efficiency differences disappear." @default.
- W58147275 created "2016-06-24" @default.
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- W58147275 date "2011-08-04" @default.
- W58147275 modified "2023-09-23" @default.
- W58147275 title "Planning with Specialized SAT Solvers" @default.
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- W58147275 doi "https://doi.org/10.1609/aaai.v25i1.7962" @default.
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