A Labelling Framework for Probabilistic Argumentation
August 01, 2017 Β· Declared Dead Β· π Annals of Mathematics and Artificial Intelligence
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Authors
Regis Riveret, Pietro Baroni, Yang Gao, Guido Governatori, Antonino Rotolo, Giovanni Sartor
arXiv ID
1708.00109
Category
cs.AI: Artificial Intelligence
Citations
23
Venue
Annals of Mathematics and Artificial Intelligence
Last Checked
4 months ago
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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