article

Complexity-sensitive decision procedures for abstract argumentation

Authors:

Wolfgang Dvořák,

Matti Järvisalo,

Johannes Peter Wallner,

Stefan WoltranAuthors Info & Claims

Artificial Intelligence, Volume 206

Pages 53 - 78

https://doi.org/10.1016/j.artint.2013.10.001

Published: 01 January 2014 Publication History

Abstract

Abstract argumentation frameworks (AFs) provide the basis for various reasoning problems in the area of Artificial Intelligence. Efficient evaluation of AFs has thus been identified as an important research challenge. So far, implemented systems for evaluating AFs have either followed a straight-forward reduction-based approach or been limited to certain tractable classes of AFs. In this work, we present a generic approach for reasoning over AFs, based on the novel concept of complexity-sensitivity. Establishing the theoretical foundations of this approach, we derive several new complexity results for preferred, semi-stable and stage semantics which complement the current complexity landscape for abstract argumentation, providing further understanding on the sources of intractability of AF reasoning problems. The introduced generic framework exploits decision procedures for problems of lower complexity whenever possible. This allows, in particular, instantiations of the generic framework via harnessing in an iterative way current sophisticated Boolean satisfiability (SAT) solver technology for solving the considered AF reasoning problems. First experimental results show that the SAT-based instantiation of our novel approach outperforms existing systems.

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cover image Artificial Intelligence

Artificial Intelligence Volume 206, Issue

January, 2014

112 pages

ISSN:0004-3702

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Copyright © Elsevier B.V. © 2013.

Publisher

Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 January 2014

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Luo MXiong JHe NXiong CWu XWu J(2024)An In-Label Prioritizing Variable Branching Strategy of SAT Solvers for a Preferred Extension of Argumentation FrameworksPRICAI 2024: Trends in Artificial Intelligence10.1007/978-981-96-0128-8_19(216-231)Online publication date: 19-Nov-2024
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