On Learning Attacks in Probabilistic Abstract Argumentation

Probabilistic argumentation combines the quantitative uncertainty accounted by probability theory with the qualitative uncertainty captured by argumentation. In this paper, we investigate the problem of learning the structure of an argumentative graph to account for (a distribution of) labellings of a set of arguments. We consider a general abstract framework, where the structure of arguments is left unspecified, and we focus on the grounded semantics. We present, with experimental insights, an anytime algorithm evaluating 'on the fly' hypothetical attacks from the examination of an input stream of labellings.