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Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability

  • Alfonso Emilio Gerevini
  • Alessandro Saetti
  • Mauro Vallati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6934)

Abstract

The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner \(\text{\sf SatPlan}\) and solver \(\text{\sf MiniSat}\) that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques.

Keywords

Machine learning for planning Planning as satisfiability 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alfonso Emilio Gerevini
    • 1
  • Alessandro Saetti
    • 1
  • Mauro Vallati
    • 1
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità degli Studi di BresciaBresciaItaly

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