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Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

Published online by Cambridge University Press:  21 September 2020

ANTONIUS WEINZIERL Open the ORCID record for ANTONIUS WEINZIERL [Opens in a new window] ,
RICHARD TAUPE Open the ORCID record for RICHARD TAUPE [Opens in a new window]  and
GERHARD FRIEDRICH
ANTONIUS WEINZIERL
Affiliation:
TU Wien (Vienna University of Technology), Austria, (e-mail: [email protected])
RICHARD TAUPE
Affiliation:
Alpen-Adria-Universität, Klagenfurt, Austria, (e-mail: [email protected]) Siemens AG Österreich, (e-mail: [email protected])
GERHARD FRIEDRICH
Affiliation:
Alpen-Adria-Universität, Klagenfurt, Austria, (e-mail: [email protected])
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Abstract

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Answer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, adaptions to the lazy-grounding setting for many important techniques, like restarts, phase saving, domain-independent heuristics, and learned-clause deletion. Furthermore, we investigate their effects and in general observe a large improvement in solving capabilities and also uncover negative effects in certain cases, indicating the need for portfolio solving as known from other solvers.

Keywords

Answer-Set ProgrammingLazy-GroundingSolving TechniquesKnowledge Representation
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 5: 36th International Conference on Logic Programming Special Issue I , September 2020 , pp. 609 - 624
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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