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A Bayesian approach to argument-based reasoning for attack estimation

Kido, Hiroyuki ORCID: and Okamoto, Keishi 2017. A Bayesian approach to argument-based reasoning for attack estimation. Presented at: 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 19-25 August 2017. Published in: Sierra, Carles ed. IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence. AAAI, pp. 249-255.

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The web is a source of a large amount of arguments and their acceptability statuses (e.g., votes for and against the arguments). However, relations existing between the fore-mentioned arguments are typically not available. This study investigates the utilisation of acceptability semantics to statistically estimate an attack relation between arguments wherein the acceptability statuses of arguments are provided. A Bayesian network model of argument-based reasoning is defined in which Dung's theory of abstract argumentation gives the substance of Bayesian inference. The model correctness is demonstrated by analysing properties of estimated attack relations and illustrating its applicability to online forums.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: AAAI
ISBN: 9780999241103
Date of Acceptance: 23 April 2017
Last Modified: 19 May 2023 13:39

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