Article: Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators
jai...@ptolemy.arc.nasa.gov
Botea, A., Enzenberger, M., Mueller, M. and Schaeffer, J. (2005)
"Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators",
Volume 24, pages 581-621.
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Abstract:
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.</p>
<p>
We have successfully used such an approach in the fourth international
planning competition IPC-4. Our system, Macro-FF, extends Hoffmann's
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.
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