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A labelling framework for probabilistic argumentation

  • Régis Riveret
  • Pietro Baroni
  • Yang Gao
  • Guido Governatori
  • Antonino Rotolo
  • Giovanni Sartor
Article

Abstract

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature.

Keywords

Probabilistic argumentation Probabilistic rule-based argumentation Probabilistic abstract argumentation Probabilistic labellings 

Mathematics Subject Classification (2010)

03H99 

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Notes

Acknowledgments

Yang Gao was supported by National Natural Science Foundation of China (NSFC) grant 61602453. Régis Riveret was supported by the Marie Curie Intra-European Fellowship PIEF-GA-2012-331472. Antonino Rotolo was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 690974 for the project MIREL: MIning and REasoning with Legal texts.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Régis Riveret
    • 1
  • Pietro Baroni
    • 2
  • Yang Gao
    • 3
  • Guido Governatori
    • 1
  • Antonino Rotolo
    • 4
  • Giovanni Sartor
    • 5
  1. 1.Data61, CSIROBrisbaneAustralia
  2. 2.DIIUniversity of BresciaBresciaItaly
  3. 3.UKP LabTechnische Universität DarmstadtDarmstadtGermany
  4. 4.CIRSFIDUniversity of BolognaBolognaItaly
  5. 5.European University InstituteFiesoleItaly

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