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Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation

Published online by Cambridge University Press:  22 November 2021

TUOMO LEHTONEN Open the ORCID record for TUOMO LEHTONEN [Opens in a new window] ,
JOHANNES P. WALLNER  and
MATTI JӒRVISALO
TUOMO LEHTONEN
Affiliation:
University of Helsinki, Helsinki, Finland (e-mail: tuomo.lehtonen@helsinki.fi)
JOHANNES P. WALLNER
Affiliation:
Graz University of Technology, Graz, Austria (e-mail: wallner@ist.tugraz.at)
MATTI JӒRVISALO
Affiliation:
University of Helsinki, Helsinki, Finland (e-mail: matti.jarvisalo@helsinki.fi)
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Abstract

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Assumption-based argumentation (ABA) is a central structured argumentation formalism. As shown recently, answer set programming (ASP) enables efficiently solving NP-hard reasoning tasks of ABA in practice, in particular in the commonly studied logic programming fragment of ABA. In this work, we harness recent advances in incremental ASP solving for developing effective algorithms for reasoning tasks in the logic programming fragment of ABA that are presumably hard for the second level of the polynomial hierarchy, including skeptical reasoning under preferred semantics as well as preferential reasoning. In particular, we develop non-trivial counterexample-guided abstraction refinement procedures based on incremental ASP solving for these tasks. We also show empirically that the procedures are significantly more effective than previously proposed algorithms for the tasks.

Keywords

answer set programmingincremental answer set solvingassumption-based argumentationstructured argumentationalgorithmsexperimental evaluation
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Special Issue 6: 37th International Conference on Logic Programming Special Issue II , November 2021 , pp. 717 - 734
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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), 2021. Published by Cambridge University Press

Footnotes

*

Work financially supported by Academy of Finland (grant 322869), University of Helsinki Doctoral Programme in Computer Science DoCS, and the Austrian Science Fund (FWF): P30168-N31 and I2854.

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