Abstract. During conversation, people often make assumptions or suppositions that are not explicitly stated. Failure to identify these suppositions may lead to mis-communication. In this paper, we describe a procedure that postulates such suppositions in the context of the discourse interpretation mechanism of BIAS – a Bayesian Interactive Argumentation System. When a belief mentioned in a user’s discourse differs from that obtained in BIAS ’ user model, our procedure searches for suppositions that explain this belief, preferring suppositions that depart minimally from the beliefs in the user model. Once a set of suppositions has been selected, it can be presented to the user for validation. Our procedure was evaluated by means of a web-based trial. Our results show that the assumptions posited by BIAS are considered sensible by our trial subjects. 1

Abstract We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user’s argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation—a Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system.