Admissibility in Probabilistic Argumentation

Nikolai KäferNikolai Käfer, Christel BaierChristel Baier, Martin DillerMartin Diller, Clemens DubslaffClemens Dubslaff, Sarah Alice GagglSarah Alice Gaggl, Holger HermannsHolger Hermanns

Nikolai Käfer, Christel Baier, Martin Diller, Clemens Dubslaff, Sarah Alice Gaggl, Holger Hermanns
Admissibility in Probabilistic Argumentation
Journal of Artificial Intelligence Research, 74:957-1004, June 2022

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KurzfassungAbstract
Abstract argumentation is a prominent reasoning framework. It comes with a variety of semantics and has lately been enhanced by probabilities to enable a quantitative treatment of argumentation. While admissibility is a fundamental notion for classical reasoning in abstract argumentation frameworks, it has barely been reflected so far in the probabilistic setting. In this paper, we address the quantitative treatment of abstract argumentation based on probabilistic notions of admissibility. Our approach follows the natural idea of defining probabilistic semantics for abstract argumentation by systematically imposing constraints on the joint probability distribution on the sets of arguments, rather than on probabilities of single arguments. As a result, there might be either a uniquely defined distribution satisfying the constraints, but also none, many, or even an infinite number of satisfying distributions are possible. We provide probabilistic semantics corresponding to the classical complete and stable semantics and show how labeling schemes provide a bridge from distributions back to argument labelings. In relation to existing work on probabilistic argumentation, we present a taxonomy of semantic notions. Enabled by the constraint-based approach, standard reasoning problems for probabilistic semantics can be tackled by SMT solvers, as we demonstrate by a proof-of-concept implementation.
Projekt:Project: CPEC, NAVAS
Forschungsgruppe:Research Group: Logische Programmierung und ArgumentationLogic Programming and Argumentation, Verifikation und formale quantitative AnalyseVerification and formal quantitative Analysis

@article{KBDDGH2022,
  author  = {Nikolai K{\"{a}}fer and Christel Baier and Martin Diller and
             Clemens Dubslaff and Sarah Alice Gaggl and Holger Hermanns},
  title   = {Admissibility in Probabilistic Argumentation},
  journal = {Journal of Artificial Intelligence Research},
  volume  = {74},
  year    = {2022},
  month   = {June},
  pages   = {957-1004},
  doi     = {https://doi.org/10.1613/jair.1.13530}
}