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Argument Revision

Argument Revision

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Original languageEnglish
Article numberexw028
Pages (from-to)2089-2134
Number of pages46
JournalJournal of Logic and Computation
Issue number7
Early online date13 Sep 2016
StatePublished - 13 Sep 2016


Understanding the dynamics of argumentation systems is a crucial component in the de-velopment of computational models of argument that are used as representations of belief. To that end, in this paper, we introduce a model of Argument Revision, presented in terms of the contraction and revision of a system of structured argumentation. Argument Revi-sion is influenced by the AGM model of belief revision, but with certain key differences. Firstly, Argument Revision involves modifying the underlying model (system of argumen-tation) from which beliefs are derived, allowing for a finer-grained approach to modifying beliefs. Secondly, the richer structure provided by a system of argumentation permits a determination of minimal change based on quantifiable effects on the system as opposed to qualitative criteria such as entrenchment orderings. Argument Revision does, however, retain a close link to the AGM approach to belief revision. A basic set of postulates for rational revisions and contractions in Argument Revision is proposed; these postulates are influenced by, and capture the spirit of, those found in AGM belief revision. After specifying a determination of minimal change, based on measurable effects on the system, we conclude the paper by going on to show how Argument Revision can be used as a strategic tool by a participant in a multi-agent dialogue, assisting with commitment retraction and dishonesty. In systems of argumentation that contain even small knowledge bases, it is difficult for a dialogue participant to fully assess the impact of seemingly trivial changes to that knowledge base, or other parts of the system; we demonstrate, by means of an example, that Argument Revision solves this problem through a determination of minimal change that is justifiable and intuitive.

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    © The Author, 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.


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