Accelerating partial order planners by improving plan and goal choices

Describes some simple domain-independent improvements to plan refinement strategies for well-founded partial order planning that promise to bring this style of planning closer to practicality. One suggestion concerns the strategy for selecting plans for refinement among the current (incomplete) candidate plans. We propose an A* heuristic that counts only steps and open conditions, while ignoring "unsafe conditions" (threats). A second suggestion concerns the strategy for selecting open conditions (goals) to be established next in a selected incomplete plan. We propose a variant of a strategy suggested by Peot and Smith (1993) and studied by Joslin and Pollack (1994); the variant gives top priority to unmatchable open conditions (enabling the elimination of the plan), second-highest priority to goals that can only be achieved uniquely and otherwise uses LIFO (last-in, first-out) prioritization. The preference for uniquely achievable goals is a "zero-commitment" strategy in the sense that the corresponding plan refinements are a matter of deductive certainty, involving no guesswork. In experiments based on modifications of UCPOP (Unsafe Conditions Partial Order Planner), we have obtained improvements by factors ranging from 5 to more than 600 for a variety of problems that are nontrivial for the unmodified version. Crucially, the hardest problems give the greatest improvements.