Enhanced Maintenance and Explanation of Expert Systems Through Explicit Models of Their Development
IEEE Transactions on Software Engineering - Special issue on artificial intelligence and software engineering
Attention, intentions, and the structure of discourse
Computational Linguistics
Plan recognition and discourse analysis: an integrated approach for understanding dialogues
Plan recognition and discourse analysis: an integrated approach for understanding dialogues
Tailoring object descriptions to a user's level of expertise
Computational Linguistics - Special issue on user modeling
Planning natural language utterances to satisfy multiple goals
Planning natural language utterances to satisfy multiple goals
Generating natural language text in response to questions about database structure
Generating natural language text in response to questions about database structure
Correcting object-related misconceptions (natural language)
Correcting object-related misconceptions (natural language)
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Planning text for advisory dialogues
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Planning coherent multisentential text
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Explanations in Knowledge Systems: Design for Explainable Expert Systems
IEEE Expert: Intelligent Systems and Their Applications
The textplanning component PIT of the LILOG system
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Integrating qualitative reasoning and text planning to generate causal explanations
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Discourse planning as an optimization process
INLG '94 Proceedings of the Seventh International Workshop on Natural Language Generation
Recognizing digressive questions during interactive generation
INLG '94 Proceedings of the Seventh International Workshop on Natural Language Generation
Content selection and organization as a process involving compromises
INLG '94 Proceedings of the Seventh International Workshop on Natural Language Generation
Pointing: a way toward explanation dialogue
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Steps from explanation planning to model construction dialogues
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
An optimizing method for structuring inferentially linked discourse
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Evaluation of neural network variable influence measures for process control
Engineering Applications of Artificial Intelligence
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Explanation is an interactive process, requiring a dialogue between advice-giver and advice-seeker. Yet current expert systems cannot participate in a dialogue with users. In particular these systems cannot clarify misunderstood explanations, elaborate on previous explanations, or respond to follow-up questions in the context of the on-going dialogue. In this paper, we describe a reactive approach to explanation - one that can participate in an on-going dialogue and employs feedback from the user to guide subsequent explanations. Our system plans explanations from a rich set of explanation strategies, recording the system's discourse goals, the plans used to achieve them, and any assumptions made while planning a response. This record provides the dialogue context the system needs to respond appropriately to the user's feedback. We illustrate our approach with examples of disambiguating a follow-up question and producing a clarifying elaboration in response to a misunderstood explanation.