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Some Foundations for Probabilistic Abstract Argumentation
Anthony Hunter
117 - 128
10.3233/978-1-61499-111-3-117
Frontiers in Artificial Intelligence and Applications
Volume 245: Computational Models of Argument
Abstract

Recently, there has been a proposal by Dung and Thang and by Li et al to extend abstract argumentation to take uncertainty of arguments into account by assigning a probability value to each argument, and then use this assignment to determine the probability that a set of arguments is an extension. In this paper, we explore some of the assumptions behind the definitions, and some of the resulting properties, of the proposal for probabilistic argument graphs.

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