SEAVE: a mechanism for verifying user presuppositions in query systems
ACM Transactions on Information Systems (TOIS)
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Presuppositions and Default Reasoning: A Study in Lexical Pragmatics
Proceedings of the First SIGLEX Workshop on Lexical Semantics and Knowledge Representation
A Probabilistic Approach for Argument Interpretation
User Modeling and User-Adapted Interaction
Incorporating a user model into an information theoretic framework for argument interpretation
UM'03 Proceedings of the 9th international conference on User modeling
Improving the presentation of argument interpretations based on user trials
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Balancing conflicting factors in argument interpretation
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
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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.