Fast planning with iterative macros

  • Authors:

  • Adi Botea;Martin Müller;Jonathan Schaeffer

  • Affiliations:

  • National ICT Australia, Australian National University, Canberra, ACT;Dept. of Computing Science, University of Alberta, Edmonton, Canada;Dept. of Computing Science, University of Alberta, Edmonton, Canada

  • Venue:
  • IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
  • Year:
  • 2007

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Abstract

Research on macro-operators has a long history in planning and other search applications. There has been a revival of interest in this topic, leading to systems that successfully combine macrooperators with current state-of-the-art planning approaches based on heuristic search. However, research is still necessary to make macros become a standard, widely-used enhancement of search algorithms. This article introduces sequences of macro-actions, called iterative macros. Iterative macros exhibit both the potential advantages (e.g., travel fast towards goal) and the potential limitations (e.g., utility problem) of classical macros, only on a much larger scale. A family of techniques are introduced to balance this trade-off in favor of faster planning. Experiments on a collection of planning benchmarks show that, when compared to low-level search and even to search with classical macro-operators, iterative macros can achieve an impressive speed-up in search.