Probabilistic Argumentation Frameworks

  • Hengfei Li
  • Nir Oren
  • Timothy J. Norman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7132)

Abstract

In this paper, we extend Dung’s seminal argument framework to form a probabilistic argument framework by associating probabilities with arguments and defeats. We then compute the likelihood of some set of arguments appearing within an arbitrary argument framework induced from this probabilistic framework. We show that the complexity of computing this likelihood precisely is exponential in the number of arguments and defeats, and thus describe an approximate approach to computing these likelihoods based on Monte-Carlo simulation. Evaluating the latter approach against the exact approach shows significant computational savings. Our probabilistic argument framework is applicable to a number of real world problems; we show its utility by applying it to the problem of coalition formation.

Keywords

Bayesian Network Multiagent System Coalition Formation Argumentation Framework Exact Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hengfei Li
    • 1
  • Nir Oren
    • 1
  • Timothy J. Norman
    • 1
  1. 1.Department of Computing ScienceUniversity of AberdeenAberdeenUK

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