Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
A computational architecture for conversation
UM '99 Proceedings of the seventh international conference on User modeling
Towards a noise-tolerant, representation-independent mechanism for argument interpretation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An information-theoretic approach for argument interpretation in a conversational setting
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A Probabilistic Approach for Argument Interpretation
User Modeling and User-Adapted Interaction
Building user argumentative models
Applied Intelligence
Modeling suppositions in users' arguments
UM'05 Proceedings of the 10th international conference on User Modeling
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We describe an argument-interpretation mechanism based on the Minimum Message Length Principle [1], and investigate the incorporation of a model of the user's beliefs into this mechanism. 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. This interpretation may differ from the user's argument in its structure and in its beliefs in the argument propositions. The results of our evaluation are encouraging, with the system generally producing plausible interpretations of users' arguments.