This paper establishes an explicit connection between formal argumentation and Bayesian inference by introducing a notion of argument and a notion of defeat among arguments in Bayesian networks. First, the two approaches are compared and it is argued that argumentation in Bayesian belief networks is a typical multi-agent affair. Since in theories of formal argumentation the so-called admissibility semantics is an important criterion of argument validity, this paper finally proposes an algorithm to decide efficiently whether a particular node is supported by an admissible argument. The proposed algorithm is then slightly extended to an algorithm that returns the top-k of strongest admissible arguments at each node. This extension is particularly interesting from a Bayesian inference point of view, because it offers a computationally tractable alternative to the NPPP-complete decision problem k-MPE (finding the top-k most probable explanations in a Bayesian network). © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Vreeswijk, G. A. W. (2005). Argumentation in Bayesian belief networks. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3366, pp. 111–129). Springer Verlag. https://doi.org/10.1007/978-3-540-32261-0_8
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