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International Conference on Scalable Uncertainty Management

SUM 2013: Scalable Uncertainty Management pp 106–119Cite as

Efficiently Estimating the Probability of Extensions in Abstract Argumentation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8078))

Abstract

Probabilistic abstract argumentation combines Dung’s abstract argumentation framework with probability theory to model uncertainty in argumentation. In this setting, we deal with the fundamental problem of computing the probability Pr sem(S) that a set S of arguments is an extension according to a semantics sem. We focus on three popular semantics (i.e., complete, grounded, and preferred) for which the state-of-the-art approach is that of estimating Pr sem(S) by using a Monte-Carlo simulation technique, as computing Pr sem(S) has been proved to be intractable. In this paper, we detect and exploit some properties of these semantics to devise a new Monte-Carlo simulation approach which is able to estimate Pr sem(S) using much fewer samples than the state-of-the-art approach, resulting in a significantly more efficient estimation technique.

The first two authors were supported by EJRM project.

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Author information

Authors and Affiliations

  1. DIMES, Università della Calabria, 87036, Rende, CS, Italy

    Bettina Fazzinga, Sergio Flesca & Francesco Parisi

Authors
  1. Bettina Fazzinga
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  2. Sergio Flesca
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  3. Francesco Parisi
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Editor information

Editors and Affiliations

  1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, BT9 5BN, Belfast, UK

    Weiru Liu

  2. Department of Computer Science, University of Maryland, 20742, College Park, MD, USA

    V. S. Subrahmanian

  3. Département d’Informatique, Université de Mons, 7000, Mons, Belgium

    Jef Wijsen

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Fazzinga, B., Flesca, S., Parisi, F. (2013). Efficiently Estimating the Probability of Extensions in Abstract Argumentation. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds) Scalable Uncertainty Management. SUM 2013. Lecture Notes in Computer Science(), vol 8078. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40381-1_9

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  • DOI: https://doi.org/10.1007/978-3-642-40381-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40380-4

  • Online ISBN: 978-3-642-40381-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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