Grabmair, M.M.GrabmairGordon, T.F.T.F.GordonWalton, D.D.Walton2022-03-112022-03-112010https://publica.fraunhofer.de/handle/publica/36952310.3233/978-1-60750-619-5-255This 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.en004conference paper