Exploiting macro-actions and predicting plan length in planning as satisfiability

  • Authors:

  • Alfonso Emilio Gerevini;Alessandro Saetti;Mauro Vallati

  • Affiliations:

  • Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Brescia, Brescia, Italy;Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Brescia, Brescia, Italy;Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Brescia, Brescia, Italy

  • Venue:
  • AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
  • Year:
  • 2011

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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 SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques.