Adi Botea, Martin Müller, and Jonathan Schaeffer
Despite major progress in AI planning over the last few years, many interesting domains remain challenging for current planners. This paper presents component abstraction, an automatic and generic technique that can reduce the complexity of an important class of planning problems. Component abstraction uses static facts in a problem definition to decompose the problem into linked abstract components. A local analysis of each component is performed to speed up planning at the component level. Our implementation uses this analysis to statically build macro operators specific to each component. A dynamic filtering process keeps for future use only the most useful macro operators. We demonstrate our ideas in Depots, Satellite, and Rovers, three standard domains used in the third AI planning competition. Our results show an impressive potential for macro operators to reduce the search complexity and achieve more stable performance.