Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks
Matthias Grabmair, Thomas F. Gordon, Douglas Walton · Frontiers in artificial intelligence and applications · 2010
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.