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)


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.


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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