Journal of Artificial Intelligence Research, 24 (2005) 581-621.
Submitted 01/05; published 10/05
© 2005 AI Access Foundation.
All rights reserved.
Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators
Department of Computing Science, University of Alberta
Edmonton, Alberta, T6G 2E8, Canada
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 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
Our results indicate a large reduction in search effort in those complex
domains where structural information can be inferred.