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Abstract Argumentation in Artificial Intelligence. Problems of Interpretation and Adequacy of Semantics for Decision Making

Bodanza, Gustavo Adrián (2015) Abstract Argumentation in Artificial Intelligence. Problems of Interpretation and Adequacy of Semantics for Decision Making. THEORIA. An International Journal for Theory, History and Foundations of Science, 30 (3). pp. 395-414. ISSN 2171-679X

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Abstract

The abstract argumentation frameworks model is currently the most used tool for characterizing the justification of defeasible arguments in Artificial Intelligence. Justifications are determined on a given attack relation among arguments and are formalized as extension semantics. In this work we argue that, contrariwise to the assumptions in that model, either some argumentation frameworks are meaningless under certain concrete definitions of the attack relation, or some of the most used extension semantics in the literature, based on the defense notion of admissibility, are not suitable in particular for the justification of arguments for decision making.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Bodanza, Gustavo Adriánbodanza@gmail.com
Additional Information: ISSN: 0495-4548 (print)
Keywords: abstract argumentation; Artificial Intelligence; attack relations; decision making
Subjects: Specific Sciences > Artificial Intelligence
General Issues > Decision Theory
Depositing User: Unnamed user with email theoria@ehu.es
Date Deposited: 08 Jun 2016 19:36
Last Modified: 08 Jun 2016 19:36
Item ID: 12158
Journal or Publication Title: THEORIA. An International Journal for Theory, History and Foundations of Science
Publisher: Euskal Herriko Unibertsitatea / Universidad del País Vasco
Official URL: http://www.ehu.eus/ojs/index.php/THEORIA/article/v...
DOI or Unique Handle: 10.1387/theoria.13150
Subjects: Specific Sciences > Artificial Intelligence
General Issues > Decision Theory
Date: September 2015
Page Range: pp. 395-414
Volume: 30
Number: 3
ISSN: 2171-679X
URI: https://philsci-archive.pitt.edu/id/eprint/12158

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