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Applying machine learning techniques to ASP solving

Maratea, Marco and Pulina, Luca and Ricca, Francesco (24 March 2012) Applying machine learning techniques to ASP solving. Alghero, University of Sassari - Computer Vision Laboratory. p. 21 (CVL 2012/003). Technical Report.

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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, we apply classification methods that, given the features of the instances in a training set and the solvers performance on these instances, inductively learn algorithm selection strategies to be applied to a test set. We report the results of a number of experiments considering solvers and different 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 significantly higher number of instances compared with any solver that entered the 3rd ASP competition.

Item Type:Technical Report
ID Code:7292
Status:Submitted
Uncontrolled Keywords:Answer Set Programming (ASP), ASP solving, applying machine learning techniques
Subjects:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Divisions:001 Università di Sassari > 02 Centri > Computer Vision Laboratory
001 Università di Sassari > 01 Dipartimenti > Economia, istituzioni e società
Publisher:University of Sassari - Computer Vision Laboratory
Deposited On:30 Mar 2012 18:11

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