A labelling framework for probabilistic argumentation

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Riveret, Regis
Baroni, Pietro
Gao, Yang
Governatori, Guido
Rotolo, Antonino
Sartor, Giovanni
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2018
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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.

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE

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83

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1

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© 2018 Springer Netherlands. This is an electronic version of an article published in Annals of Mathematics and Artificial Intelligence, May 2018, Volume 83, Issue 1, pp 21–71. Annals of Mathematics and Artificial Intelligence is available online at: http://link.springer.com/ with the open URL of your article.

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Artificial intelligence

Applied mathematics

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