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Optimizing Answer Set Computation via Heuristic-Based Decomposition

Published online by Cambridge University Press:  28 February 2019

FRANCESCO CALIMERI Open the ORCID record for FRANCESCO CALIMERI [Opens in a new window] ,
SIMONA PERRI Open the ORCID record for SIMONA PERRI [Opens in a new window]  and
JESSICA ZANGARI
FRANCESCO CALIMERI*
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: calimeri@mat.unical.it, perri@mat.unical.it, zangari@mat.unical.it)
SIMONA PERRI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: calimeri@mat.unical.it, perri@mat.unical.it, zangari@mat.unical.it)
JESSICA ZANGARI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: calimeri@mat.unical.it, perri@mat.unical.it, zangari@mat.unical.it)
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Abstract

Answer Set Programming (ASP) is a purely declarative formalism developed in the field of logic programming and non-monotonic reasoning: computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. Different, semantically equivalent, programs can be defined for the same problem; however, performance of systems evaluating them might significantly vary. We propose an approach for automatically transforming an input logic program into an equivalent one that can be evaluated more efficiently. One can make use of existing tree-decomposition techniques for rewriting selected rules into a set of multiple ones; the idea is to guide and adaptively apply them on the basis of proper new heuristics, to obtain a smart rewriting algorithm to be integrated into an ASP system. The method is rather general: it can be adapted to any system and implement different preference policies. Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation; we use them in order to implement the approach into the ASP system DLV, in particular into its grounding subsystem ℐ-DLV, and carry out an extensive experimental activity for assessing the impact of the proposal.

Keywords

knowledge representation and nonmonotonic reasoninglogic programming methodology and applicationsdatabases and semantic web reasoningAnswer Set Programming
Type
Rapid Communication
Information
Theory and Practice of Logic Programming , Volume 19 , Issue 4 , July 2019 , pp. 603 - 628
Copyright
© Cambridge University Press 2019 

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Footnotes

*

This work has been partially supported by the Italian region Calabria under project “DLV Large Scale” (CUP J28C17000220006) POR Calabria FESR 2014–2020 and by both the European Union and the Italian Ministry of Economic Development under the project EU H2020 PON I&C 2014–2020 “Smarter Solutions in the Big Data World – S2BDW” (CUP B28I17000250008). This work is the extended version of a paper originally appeared in the Proceedings of 20th Symposium on Practical Aspects of Declarative Languages (PADL 2018), January 8–9, 2018, Los Angeles, USA. Program chairs were Kevin Hamlen and Nicola Leone. The paper presents new material that integrates and extends what has been reported in the original paper; in particular, it provides the reader with proper preliminaries (omitted in the original paper for space constraints), more detailed discussions on the proposed techniques and richer comparisons with related approaches, along with an extended number of examples. Furthermore, a more thorough experimental activity is presented, discussed in part in the main text and in part in Appendices in the Supplementary Material, that covers also new domains that were unexplored in the original paper.

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