Attention, intentions, and the structure of discourse
Computational Linguistics
Understanding Bayesian reasoning via graphical displays
CHI '89 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
A “natural logic” for natural language processing and knowledge representation
A “natural logic” for natural language processing and knowledge representation
The repair of speech act misunderstandings by abductive inference
Computational Linguistics
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Generalized augmented transition network grammars for generation from semantic networks
Computational Linguistics
Explanation structures in XSEL
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Discourse pragmatics and ellipsis resolution in task-oriented natural language interfaces
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Building intelligent dialog systems
intelligence
User-Tailored Planning of Mixed Initiative Information-Seeking Dialogues
User Modeling and User-Adapted Interaction
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
Adaptations of multimodal content in dialog systems targeting heterogeneous devices
UM'03 Proceedings of the 9th international conference on User modeling
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We describe the natural language processing and knowledge representation components of B2, a collaborative system that allows medical students to practice their decision-making skills by considering a number of medical cases that differ from each other in a controlled manner. The underlying decision-support model of B2 uses a Bayesian network that captures the results of prior clinical studies of abdominal pain. B2 generates story-problems based on this model and supports natural language queries about the conclusions of the model and the reasoning behind them. B2 benefits from having a single knowledge representation and reasoning component that acts as a blackboard for intertask communication and cooperation. All knowledge is represented using a propositional semantic network formalism, thereby providing a uniform representation to all components. The natural language component is composed of a generalized augmented transition network parser/grammar and a discourse analyzer for managing the natural language interactions. The knowlege representation component supports the natural language component by providing a uniform representation of the content and structure of the interaction, at the parser, discourse, and domain levels. This uniform representation allows distinct tasks, such as dialog management, domain-specific reasoning, and meta-reasoning about the Bayesian network, to all use the same information source, without requiring mediation. This is important because there are queries, such as Why?, whose interpretation and response requires information from each of these tasks. By contrast, traditional approaches treat each subtask as a “black-box” with respect to other task components, and have a separate knowledge representation language for each. As a result, they have had much more difficulty providing useful responses.