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  • A labelling framework for probabilistic argumentation

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    Governatori161229.pdf (553.0Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Riveret, Regis
    Baroni, Pietro
    Gao, Yang
    Governatori, Guido
    Rotolo, Antonino
    Sartor, Giovanni
    Griffith University Author(s)
    Governatori, Guido
    Year published
    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 ...
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    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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    Journal Title
    ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
    Volume
    83
    Issue
    1
    DOI
    https://doi.org/10.1007/s10472-018-9574-1
    Copyright Statement
    © 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.
    Subject
    Artificial intelligence
    Applied mathematics
    Publication URI
    http://hdl.handle.net/10072/383056
    Collection
    • Journal articles

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