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Applying machine learning techniques to ASP solving

Maratea, Marco and Pulina, Luca and Ricca, Francesco (2012) Applying machine learning techniques to ASP solving. In: 28th International Conference on Logic Programming (ICLP'12), 4-8 September 2012, Budapest, Ungheria. [S.l.], Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik. p. 37-48. (Leibniz International Proceedings in Informatics, 17). ISSN 1868-8969. ISBN 978-3-939897-43-9. Conference or Workshop Item.

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DOI: 10.4230/LIPIcs.ICLP.2012.37

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Abstract

Having in mind the task of improving the solving methods for Answer Set Programming (ASP), there are two usual ways to reach this goal: (i) extending state-of-the-art techniques and ASP solvers, or (ii) designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of-the-art solvers, and to apply machine learning techniques for choosing automatically the “best” available solver on a per-instance basis.
In this paper we pursue this latter direction. We first define a set of cheap-to-compute syntactic features that characterize several aspects of ASP programs. Then, given the features of the instances in a training set and the solvers performance on these instances, we apply a classification method to inductively learn algorithm selection strategies to be applied to a test set. We report the results of an experiment considering solvers and training and test sets of instances taken from the ones submitted to the “System Track” of the 3rd ASP competition. Our analysis shows that, by applying machine learning techniques to ASP solving, it is possible to obtain very robust performance: our approach can solve a higher number of instances compared with any solver that entered the 3rd ASP competition.

Item Type:Conference or Workshop Item (Contribute)
ID Code:7972
Status:Published
Uncontrolled Keywords:Answer set programming, automated algorithm selection, multi-engine solvers
Subjects:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Divisions:001 Università di Sassari > 01-a Nuovi Dipartimenti dal 2012 > Scienze Politiche, Scienze della Comunicazione e Ingegneria dell'Informazione
Publisher:Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik
ISSN:1868-8969
Copyright Holders:© Marco Maratea, Luca Pulina, and Francesco Ricca
ISBN:978-3-939897-43-9
Deposited On:20 Sep 2012 09:35

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