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Boosting Answer Set Optimization with Weighted Comparator Networks

Published online by Cambridge University Press:  11 May 2020

JORI BOMANSON
Affiliation:
Department of Computer Science, Aalto University, FI-00076, AALTO, Finland, (e-mail: Jori.Bomanson@aalto.fi)
TOMI JANHUNEN
Affiliation:
Department of Computer Science, Aalto University, FI-00076, AALTO, Finland and Information Technology and Communication Sciences, Tampere UniversityFI-33014, Finland, (e-mail: Tomi.Janhunen@tuni.fi)
Corresponding

Abstract

Answer set programming (ASP) is a paradigm for modeling knowledge-intensive domains and solving challenging reasoning problems. In ASP solving, a typical strategy is to preprocess problem instances by rewriting complex rules into simpler ones. Normalization is a rewriting process that removes extended rule types altogether in favor of normal rules. Recently, such techniques led to optimization rewriting in ASP, where the goal is to boost answer set optimization by refactoring the optimization criteria of interest. In this paper, we present a novel, general, and effective technique for optimization rewriting based on comparator networks which are specific kinds of circuits for reordering the elements of vectors. The idea is to connect an ASP encoding of a comparator network to the literals being optimized and to redistribute the weights of these literals over the structure of the network. The encoding captures information about the weight of an answer set in auxiliary atoms in a structured way that is proven to yield exponential improvements during branch-and-bound optimization on an infinite family of example programs. The used comparator network can be tuned freely, for example, to find the best size for a given benchmark class. Experiments show accelerated optimization performance on several benchmark problems.

Type
Original Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

*

We would like to thank the anonymous reviewers and Dr. Martin Gebser for their valuable comments and suggestions. This work has been supported in part by the Finnish Centre of Excellence in Computational Inference Research (COIN) (Academy of Finland, project #251170). Moreover, Jori Bomanson has been supported by the Helsinki Doctoral Education Network in Information and Communication Technology (HICT) and Tomi Janhunen partially by the Academy of Finland project Ethical AI for the Governance of Society (ETAIROS, grant #327352).

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