research-article

On the Complexity of Probabilistic Abstract Argumentation Frameworks

Authors:

Bettina Fazzinga,

Francesco ParisiAuthors Info & Claims

ACM Transactions on Computational Logic (TOCL), Volume 16, Issue 3

Article No.: 22, Pages 1 - 39

https://doi.org/10.1145/2749463

Published: 02 June 2015 Publication History

Abstract

Probabilistic abstract argumentation combines Dung’s abstract argumentation framework with probability theory in order to model uncertainty in argumentation. In this setting, we address the fundamental problem of computing the probability that a set of arguments is an extension according to a given semantics. We focus on the most popular semantics (i.e., admissible, stable, complete, grounded, preferred, ideal-set, ideal, stage, and semistable) and show the following dichotomy result: computing the probability that a set of arguments is an extension is either FP or FP^#P-complete depending on the semantics adopted. Our polynomial-time results are particularly interesting, as they hold for some semantics for which no polynomial-time technique was known so far.

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Cited By

Bistarelli SDavid VSantini FTaticchi C(2024)Temporal duration-based probabilistic argumentation frameworksJournal of Logic and Computation10.1093/logcom/exae039Online publication date: 31-Jul-2024
https://doi.org/10.1093/logcom/exae039
Fichte JHecher MMahmood YMeier AElkind E(2023)Quantitative reasoning and structural complexity for claim-centric argumentationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/358(3212-3220)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/358
Alfano GGreco SParisi FTrubitsyna IElkind E(2023)Preferences and constraints in abstract argumentationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/345(3095-3103)Online publication date: 19-Aug-2023
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Index Terms

On the Complexity of Probabilistic Abstract Argumentation Frameworks
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Theorem proving algorithms
2. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Transactions on Computational Logic

ACM Transactions on Computational Logic Volume 16, Issue 3

July 2015

285 pages

ISSN:1529-3785

EISSN:1557-945X

DOI:10.1145/2764956

Editor:
Dale Miller
INRIA Saclay, France

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Copyright © 2015 ACM.

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Publication History

Published: 02 June 2015

Accepted: 01 March 2015

Revised: 01 January 2015

Received: 01 May 2014

Published in TOCL Volume 16, Issue 3

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Cited By

Bistarelli SDavid VSantini FTaticchi C(2024)Temporal duration-based probabilistic argumentation frameworksJournal of Logic and Computation10.1093/logcom/exae039Online publication date: 31-Jul-2024
https://doi.org/10.1093/logcom/exae039
Fichte JHecher MMahmood YMeier AElkind E(2023)Quantitative reasoning and structural complexity for claim-centric argumentationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/358(3212-3220)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/358
Alfano GGreco SParisi FTrubitsyna IElkind E(2023)Preferences and constraints in abstract argumentationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/345(3095-3103)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/345
Alfano GGreco SParisi FTrubitsyna IWilliams BChen YNeville J(2023)Abstract argumentation framework with conditional preferencesProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i5.25766(6218-6227)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i5.25766
Alfano GCalautti MGreco SParisi FTrubitsyna I(2023)Explainable acceptance in probabilistic and incomplete abstract argumentation frameworksArtificial Intelligence10.1016/j.artint.2023.103967323(103967)Online publication date: Oct-2023
https://doi.org/10.1016/j.artint.2023.103967
Fazzinga BFlesca SFurfaro F(2023)Taking into account “who said what” in abstract argumentation: Complexity resultsArtificial Intelligence10.1016/j.artint.2023.103885318(103885)Online publication date: May-2023
https://doi.org/10.1016/j.artint.2023.103885
Baumeister DHogrebe T(2023)On the Complexity of Predicting Election Outcomes and Estimating Their RobustnessSN Computer Science10.1007/s42979-023-01725-04:4Online publication date: 28-Apr-2023
https://dl.acm.org/doi/10.1007/s42979-023-01725-0
Fazzinga BFlesca SFurfaro FPontieri L(2022)Process Mining meets argumentationInformation Systems10.1016/j.is.2022.101987107:COnline publication date: 1-Jul-2022
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Bistarelli SSantini F(2022)A Definition of�Sceptical Semantics in�the�Constellations ApproachLogic Programming and Nonmonotonic Reasoning10.1007/978-3-031-15707-3_6(62-74)Online publication date: 5-Sep-2022
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Esca�uela Gonzalez MBud�n MSimari GSimari G(2021)Labeled Bipolar Argumentation FrameworksJournal of Artificial Intelligence Research10.1613/jair.1.1239470(1557-1636)Online publication date: 1-May-2021
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