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 > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering
Depositing User:	Mauro Vallati
Date Deposited:	11 Oct 2012 14:30
Last Modified:	28 Aug 2021 20:27
URI:	https://eprints.hud.ac.uk/id/eprint/15351

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