A Bayesian model of plan recognition
Artificial Intelligence
Procedural help in Andes: generating hints using a Bayesian network student model
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Bayesian reasoning in an abductive mechanism for argument generation and analysis
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The Architecture of Cognition
Exploratory Interaction with a Bayesian Argumentation System
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A process model for recognizing communicative acts and modeling negotiation subdialogues
Computational Linguistics
Arguing about planning alternatives
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
Towards the generation of rebuttals in a Bayesian Argumentation System
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Recognizing intentions from rejoinders in a Bayesian interactive argumentation system
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Inferring informational goals from free-text queries: a Bayesian approach
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Argument Interpretation Using Minimum Message Length
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
An annotation scheme for cross-cultural argumentation and persuasion dialogues
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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This paper describes an integrated approach for interpreting a user's responses and generating replies in the framework of a WWW-based Bayesian argumentation system. Our system consults a user model which represents a user's beliefs, inferences and attentional focus, as well as the system's certainty regarding the user's beliefs. The interpretation mechanism takes into account these factors to infer the intended effect of the user's response on the system's argument. The reply-generation mechanism focuses on the identification of discrepancies between the beliefs in the user model and the beliefs held by the system that are relevant to the inferred interpretation.