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
A computational architecture for conversation
UM '99 Proceedings of the seventh international conference on User modeling
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
An Integrated Approach for Generating Arguments and Rebuttals and Understanding Rejoinders
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
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We describe an argument interpretation mechanism which receives as input a segmented argument composed of Natural Language sentences, and employs the Minimum Message Length Principle to select an interpretation among candidate options. This principle enables our mechanism to cope with noisy input in terms of wording, beliefs and argument structure. The performance of our system was evaluated by distorting automatically generated arguments, and passing them to the system for interpretation. Our evaluation showed that in most cases, the interpretations produced by the system matched precisely or almost-precisely the representation of the original arguments.