Computer Science – Artificial Intelligence
Scientific paper
2011-09-09
Journal Of Artificial Intelligence Research, Volume 24, pages 581-621, 2005
Computer Science
Artificial Intelligence
Scientific paper
10.1613/jair.1696
Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmanns state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.
Botea Adi
Enzenberger M.
Mueller Marcus
Schaeffer Jack
No associations
LandOfFree
Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-38174