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European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty

ECSQARU 2015: Symbolic and Quantitative Approaches to Reasoning with Uncertainty pp 83-92 | Cite as

Explaining Bayesian Networks Using Argumentation

  • Sjoerd T. TimmerEmail author
  • John-Jules Ch. Meyer
  • Henry Prakken
  • Silja Renooij
  • Bart Verheij
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9161)

Abstract

Qualitative and quantitative systems to deal with uncertainty coexist. Bayesian networks are a well known tool in probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated they may appear as black box models. Argumentation models, on the contrary, emphasise the derivation of results. However, they have notorious difficulty dealing with probabilities. In this paper we formalise a two-phase method to extract probabilistically supported arguments from a Bayesian network. First, from a BN we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the evidence modelled in the Bayesian network.

Keywords

Bayesian networks Argumentation Reasoning Explanation Inference Uncertainty 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sjoerd T. Timmer
    • 1
    Email author
  • John-Jules Ch. Meyer
    • 1
  • Henry Prakken
    • 1
    • 2
  • Silja Renooij
    • 1
  • Bart Verheij
    • 3
  1. 1.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands
  2. 2.Faculty of LawUniversity of GroningenGroningenThe Netherlands
  3. 3.Artificial Intelligence InstituteUniversity of GroningenGroningenThe Netherlands

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