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The External Interface for Extending WASP

Published online by Cambridge University Press:  10 December 2018

CARMINE DODARO Open the ORCID record for CARMINE DODARO [Opens in a new window]  and
FRANCESCO RICCA Open the ORCID record for FRANCESCO RICCA [Opens in a new window]
CARMINE DODARO*
Affiliation:
DIBRIS, University of Genova, Genova, Italy (e-mail: dodaro@dibris.unige.it)
FRANCESCO RICCA
Affiliation:
DEMACS, University of Calabria, Rende, Italy (e-mail: ricca@mat.unical.it)
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Abstract

Answer set programming (ASP) is a successful declarative formalism for knowledge representation and reasoning. The evaluation of ASP programs is nowadays based on the conflict-driven clause learning (CDCL) backtracking search algorithm. Recent work suggested that the performance of CDCL-based implementations can be considerably improved on specific benchmarks by extending their solving capabilities with custom heuristics and propagators. However, embedding such algorithms into existing systems requires expert knowledge of the internals of ASP implementations. The development of effective solver extensions can be made easier by providing suitable programming interfaces. In this paper, we present the interface for extending the CDCL-based ASP solver wasp. The interface is both general, that is, it can be used for providing either new branching heuristics or propagators, and external, that is, the implementation of new algorithms requires no internal modifications of wasp. Moreover, we review the applications of the interface witnessing it can be successfully used to extend wasp for solving effectively hard instances of both real-world and synthetic problems.

Keywords

knowledge representation and reasoninganswer set programmingapplication programming interfacepropagatorschoice heuristics

Information

Type
Technical Note
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 2 , March 2020 , pp. 225 - 248
Copyright
© Cambridge University Press 2018 

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Footnotes

*

The paper has been partially supported by the Italian Ministry for Economic Development (MISE) under project “PIUCultura – Paradigmi Innovativi per l’Utilizzo della Cultura” (no. F/020016/01-02/X27), and under project “Smarter Solutions in the Big Data World (S2BDW)” (no. F/050389/01-03/X32) funded within the call “HORIZON2020” PON I&C 2014-2020. Authors are grateful to Mario Alviano, Bernardo Cuteri, Philip Gasteiger, Nicola Leone, Benjamin Musitsch, Peter Schüller, and Kostyantyn Shchekotykhin for their suggestions.

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