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Modelling Uncertainty in Persuasion

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Scalable Uncertainty Management (SUM 2013)

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

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Abstract

Participants in argumentation often have some doubts in their arguments and/or the arguments of the other participants. In this paper, we model uncertainty in beliefs using a probability distribution over models of the language, and use this to identify which are good arguments (i.e. those with support with a probability on or above a threshold). We then investigate three strategies for participants in dialogical argumentation that use this uncertainty information. The first is an exhaustive strategy for presenting a participant’s good arguments, the second is a refinement of the first that selects the good arguments that are also good arguments for the opponent, and the third selects any argument as long as it is a good argument for the opponent. We show that the advantage of the second strategy is that on average it results in shorter dialogues than the first strategy, and the advantage of the third strategy is that under some general circumstances the participant can always win the dialogue.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    Anthony Hunter

Authors
  1. Anthony Hunter
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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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© 2013 Springer-Verlag Berlin Heidelberg

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Hunter, A. (2013). Modelling Uncertainty in Persuasion. 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_5

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  • DOI: https://doi.org/10.1007/978-3-642-40381-1_5

  • 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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