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Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks
Matthias Grabmair, Thomas F. Gordon, Douglas Walton
255 - 266
10.3233/978-1-60750-619-5-255
Frontiers in Artificial Intelligence and Applications
Volume 216: Computational Models of Argument
Abstract

This paper presents a technique with which instances of argument structures in the Carneades model can be given a probabilistic semantics by translating them into Bayesian networks. The propagation of argument applicability and statement acceptability can be expressed through conditional probability tables. This translation suggests a way to extend Carneades to improve its utility for decision support in the presence of uncertainty.

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