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

Gerevini, Alfonso Emilio, Saetti, Alessandro and Vallati, Mauro (2015) Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability. AI Communications, 28 (2). pp. 323-344. ISSN 0921-7126

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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: 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 demonstrating that significant speedups can be obtained.

Item Type: Article
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
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Depositing User: Mauro Vallati
Date Deposited: 05 Jun 2014 13:25
Last Modified: 28 Aug 2021 19:11


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