Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks

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. Keywords. Carneades, argumentation and probability, Bayesian Networks

• Type: Journal
• Year: 2010
• Where: COMMA
• Authors: Matthias Grabmair, Thomas F. Gordon, Douglas Walton
• Keywords: Applied Computing, Bayesian Networks, Carneades, COMMA 2010, Conditional Probability Tables