On Relating Abstract and Structured Probabilistic Argumentation: A Case Study

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)


This paper investigates the relations between Timmer et al.’s proposal for explaining Bayesian networks with structured argumentation and abstract models of probabilistic argumentation. First some challenges are identified for incorporating probabilistic notions of argument strength in structured models of argumentation. Then it is investigated to what extent Timmer et al’s approach meets these challenges and satisfies semantics and rationality conditions for probabilistic argumentation frameworks proposed in the literature. The results are used to draw conclusions about the strengths and limitations of both approaches.


Bayesian Network Structure Argumentation Argumentation Framework Default Reasoning Probative Force 
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.


  1. 1.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and n-person games. Artif. Intell. 77, 321–357 (1995)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Dung, P.M., Thang, P.M.: Towards (probabilistic) argumentation for jury-based dispute resolution. In: Baroni, P., Cerutti, F., Giacomin, M., Simari, G.R. (eds.) Computational Models of Argument. Proceedings of COMMA 2010, pp. 171–182. IOS Press, Amsterdam (2010)Google Scholar
  3. 3.
    Hahn, U., Hornikx, J.: A normative framework for argument quality: argumentation schemes with a Bayesian foundation. Synthese 193, 1833–1873 (2016)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Hunter, A.: A probabilistic approach to modelling uncertain logical arguments. Int. J. Approx. Reason. 54, 47–81 (2013)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Hunter, A.: Probabilistic qualification of attack in abstract argumentation. Int. J. Approx. Reason. 55, 607–638 (2014)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Hunter, A., Thimm, M.: On partial information and contradictions in probabilistic abstract argumentation. In: Principles of Knowledge Representation and Reasoning: Proceedings of the Fifteenth International Conference (KR-16), pp. 53–62. AAAI Press (2016)Google Scholar
  7. 7.
    Li, H., Oren, N., Norman, T.: Probabilistic argumentation frameworks. In: Proceedings First Workshop on the Theory and Applications of Formal Argument, pp. 1–16 (2011)Google Scholar
  8. 8.
    Modgil, S., Prakken, H.: A general account of argumentation with preferences. Artif. Intell. 195, 361–397 (2013)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Thimm, M.: A probabilistic semantics for abstract argumentation. In: Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 750–755 (2012)Google Scholar
  10. 10.
    Timmer, S., Meyer, J.-J.C., Prakken, H., Renooij, S., Verheij, B.: A two-phase method for extracting explanatory arguments from Bayesian networks. Int. J. Approx. Reason. 80, 475–494 (2017)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands
  2. 2.Faculty of LawUniversity of GroningenGroningenThe Netherlands

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