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Constraints, lazy constraints, or propagators in ASP solving: An empirical analysis*

Published online by Cambridge University Press:  24 August 2017

BERNARDO CUTERI ,
CARMINE DODARO Open the ORCID record for CARMINE DODARO [Opens in a new window] ,
FRANCESCO RICCA  and
PETER SCHÜLLER Open the ORCID record for PETER SCHÜLLER [Opens in a new window]
BERNARDO CUTERI
Affiliation:
DeMaCS, University of Calabria, Italy (e-mail: cuteri@mat.unical.it)
CARMINE DODARO
Affiliation:
DIBRIS, University of Genova, Italy (e-mail: dodaro@dibris.unige.it)
FRANCESCO RICCA
Affiliation:
DeMaCS, University of Calabria, Italy (e-mail: ricca@mat.unical.it)
PETER SCHÜLLER
Affiliation:
Faculty of Engineering, Marmara University, Turkey Institute of Information Systems, Knowledge-based Systems Group, TU Wien, Austria (e-mail: schueller.p@gmail.com)
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Abstract

Answer set programming (ASP) is a well-established declarative paradigm. One of the successes of ASP is the availability of efficient systems. State-of-the-art systems are based on the ground+solve approach. In some applications, this approach is infeasible because the grounding of one or a few constraints is expensive. In this paper, we systematically compare alternative strategies to avoid the instantiation of problematic constraints, which are based on custom extensions of the solver. Results on real and synthetic benchmarks highlight some strengths and weaknesses of the different strategies.

Keywords

Answer set programminglazy groundingpropagators
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 17 , Special Issue 5-6: 33rd International Conference on Logic Programming , September 2017 , pp. 780 - 799
Copyright
Copyright © Cambridge University Press 2017 

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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), 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, and by the Scientific and Technological Research Council of Turkey (TUBITAK) Grant 114E777.

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