Gerevini, Alfonso, Saetti, Alessandro and Vallati, Mauro (2012) Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability. In: 19th RCRA workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, 14th - 15th June 2012, Rome, Italy.
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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 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:||Conference or Workshop Item (Paper)|
|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:||25 Oct 2012 11:32|
|Last Modified:||25 Oct 2012 11:32|
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