  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.
