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Macro Learning in Planning as Parameter Configuration

  • Maher Alhossaini
  • J. Christopher Beck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)

Abstract

In AI planning, macro learning is the task of finding sub-sequences of operators that can be added to the planning domain to improve planner performance. Typically, a single set is added to the domain for all problem instances. A number of techniques have been developed to generate such a macro set based on offline analysis of problem instances. We build on recent work on instance-specific and fixed-set macros, and recast the macro generation problem as parameter configuration: the macros in a domain are viewed as parameters of the planning problem. We then apply an existing parameter configuration system to reconfigure a domain either once or per problem instance. Our empirical results demonstrate that our approach outperforms existing macro acquisition and filtering tools. For instance-specific macros, our approach almost always achieves equal or better performance than a complete evaluation approach, while often being an order of magnitude faster offline.

Keywords

Planning Macro Learning Parameter Configuration Machine Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maher Alhossaini
    • 1
  • J. Christopher Beck
    • 2
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada
  2. 2.Department of Mechanical & Industrial EngineeringUniversity of TorontoTorontoCanada

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