Engineering Efficient Planners with SAT

Abstract

Planning with SAT has long been viewed as a main approach to AI planning. In comparison to other approaches, its high memory requirements have been considered to be a main obstacle to its scalability to large planning problems. Better implementation technology, especially addressing the memory use, together with a shift of understanding about SAT-based planning during the past ten years, enables planners that radically differ from those from the late 1990s. We discuss a SAT-based planning system that implements modern versions of virtually all components of first planners that used SAT, focusing on the new implementation technology for a compact clause representation that is both simpler and more effective than ones proposed earlier. Specifically, the decreased memory requirements enable the use of top-level solution strategies that lift the performance of SAT-based planning to the same level with other search methods.