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The Hidden Power of Abstract Argumentation Semantics

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Theory and Applications of Formal Argumentation (TAFA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9524))

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Abstract

Abstract argumentation plays an important role in many advanced AI formalisms. It is thus vital to understand the strengths and limits of the different semantics available. In this work, we contribute to this line of research and investigate two recently proposed properties: rejected arguments and implicit conflicts. Given an argumentation framework F, the former refers to arguments in F which do not occur in any extension of F; the latter refers to pairs of arguments which do not occur together in any extension of F despite not being linked in F’s attack relation. We consider four prominent semantics, viz. stable, preferred, semi-stable and stage and show that their expressive power relies on both properties. Among our results, we refute a recent conjecture by Baumann et al. on implicit conflicts.

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Acknowledgements

This research has been supported by the Austrian Science Fund (FWF) through projects I1102 and P25521.

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Authors and Affiliations

  1. TU Wien, Vienna, Austria

    Thomas Linsbichler, Christof Spanring & Stefan Woltran

  2. University of Liverpool, Liverpool, UK

    Christof Spanring

Authors
  1. Thomas Linsbichler
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  2. Christof Spanring
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  3. Stefan Woltran
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Corresponding author

Correspondence to Thomas Linsbichler .

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Editors and Affiliations

  1. King's College London, London, United Kingdom

    Elizabeth Black

  2. Strand Building;Strand Campus, King's College London, London, United Kingdom

    Sanjay Modgil

  3. Department of Computing Science, University of Aberdeen, Aberdeen, United Kingdom

    Nir Oren

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© 2015 Springer International Publishing Switzerland

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Linsbichler, T., Spanring, C., Woltran, S. (2015). The Hidden Power of Abstract Argumentation Semantics. In: Black, E., Modgil, S., Oren, N. (eds) Theory and Applications of Formal Argumentation. TAFA 2015. Lecture Notes in Computer Science(), vol 9524. Springer, Cham. https://doi.org/10.1007/978-3-319-28460-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-28460-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28459-0

  • Online ISBN: 978-3-319-28460-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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