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Probabilistic Argumentation Frameworks – A Logical Approach

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Scalable Uncertainty Management (SUM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8720))

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Abstract

Abstract argumentation is nowadays a vivid field within artificial intelligence and has seen different developments recently. In particular, enrichments of the standard Dung frameworks have been proposed in order to model scenarios where probabilities or uncertain information have to be expressed. As for standard approaches of abstract argumentation, a uniform logical formalization for such frameworks is of great help in order to understand and compare different approaches. In this paper, we take a first step in this direction and characterize different semantics from the approach of Li et al in terms of probabilistic logic. This not only provides a uniform logical formalization but also might pave the way for future implementations.

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Author information

Authors and Affiliations

  1. Computer Science and Communications, University of Luxembourg, Rue Richard Coudenhove-Kalergi 6, L-1359, Luxembourg

    Dragan Doder

  2. Institute of Information Systems, Vienna University of Technology, Favoritenstrasse 9–11, 1040, Vienna, Austria

    Stefan Woltran

Authors
  1. Dragan Doder
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  2. Stefan Woltran
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Editor information

Editors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione (ISTI - CNR), Pisa, Italy

    Umberto Straccia

  2. Department of Computer Science and Information Systems, University of London, London, United Kingdom

    Andrea Calì

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Doder, D., Woltran, S. (2014). Probabilistic Argumentation Frameworks – A Logical Approach. In: Straccia, U., Calì, A. (eds) Scalable Uncertainty Management. SUM 2014. Lecture Notes in Computer Science(), vol 8720. Springer, Cham. https://doi.org/10.1007/978-3-319-11508-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-11508-5_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11507-8

  • Online ISBN: 978-3-319-11508-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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