A labelling framework for probabilistic argumentation
Annals of mathematics and artificial intelligence, 2018, Vol. 83, No. 1, pp. 21-71
RIVERET, Régis, BARONI, Pietro, GAO, Yang, GOVERNATORI, Guido, ROTOLO, Antonino, SARTOR, Giovanni, A labelling framework for probabilistic argumentation, Annals of mathematics and artificial intelligence, 2018, Vol. 83, No. 1, pp. 21-71 - http://hdl.handle.net/1814/59986
Retrieved from Cadmus, EUI Research Repository
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.
First online: 20 March 2018
Cadmus permanent link: http://hdl.handle.net/1814/59986
Full-text via DOI: 10.1007/s10472-018-9574-1
ISSN: 1012-2443; 1573-7470
Publisher: Springer (part of Springer Nature)
Keyword(s): Probabilistic argumentation Probabilistic rule-based argumentation Probabilistic abstract argumentation Probabilistic labellings Abstract argumentation Bayesian networks Defeasible logic Semantics Tutorial aspic(+) Systems Support Prism
Sponsorship and Funder information:National Natural Science Foundation of China (NSFC) Marie Curie Intra-European Fellowship [PIEF-GA-2012-331472]European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant 
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