Heuristic Planning with SAT: Beyond Uninformed Depth-First Search

Abstract. 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 conflictdirected clause learning (CDCL) framework. In this work we consider alternatives to pure depth-first search, and show that carefully chosen randomized search order, which is not strictly depth-first, allows to leverage the intrinsic strengths of CDCL better, and will lead to a planner that clearly outperforms existing planners.

• Type: Journal
• Year: 2010
• Where: AUSAI
• Authors: Jussi Rintanen
• Keywords: Artificial Intelligence, AUSAI 2010, Depth-first Search, Dominant Heuristic State-space, Heuristics