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

Gerevini, Alfonso Emilio, Saetti, Alessandro and Vallati, Mauro (2011) Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability. In: AI*IA 2011: Artificial Intelligence Around Man and Beyond. Lecture Notes in Computer Science, 6934 . Springer, London, UK, pp. 189-200. ISBN 978-3-642-23953-3

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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.

    Item Type: Book Chapter
    Subjects: Q Science > Q Science (General)
    Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics > QA76 Computer software
    Schools: School of Computing and Engineering > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
    School of Computing and Engineering
    Depositing User: Mauro Vallati
    Date Deposited: 11 Oct 2012 15:30
    Last Modified: 11 Oct 2012 15:30
    URI: http://eprints.hud.ac.uk/id/eprint/15351

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