Generating explanatory discourse
Current research in natural language generation
The role of the user's domain knowledge in generation
Computational Intelligence
Automated discourse generation using discourse structure relations
Artificial Intelligence - Special volume on natural language processing
Plan-based integration of natural language and graphics generation
Artificial Intelligence - Special volume on natural language processing
A Model for Adapting Explanations to the User‘s Likely Inferences
User Modeling and User-Adapted Interaction
Proof verbalization as an application of NLG
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
P.rex: An Interactive Proof Explainer
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
A review of explanation methods for heuristic expert systems
The Knowledge Engineering Review
A generic modular data structure for proof attempts alternating on ideas and granularity
MKM'05 Proceedings of the 4th international conference on Mathematical Knowledge Management
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In order to generate high quality explanations in mathematical domains, the presentation must be adapted to the knowledge of the intended audience. Most proof presentation systems only communicate proofs on a fixed degree of abstraction independently of the addressee's knowledge. In this paper, we shall present the proof explanation system P.rex. Based on assumptions about the addressee's knowledge, its dialog planner chooses a degree of abstraction for each proof step to be explained. In reaction to the user's interactions, which are allowed at any time, it enters clarification dialogs to revise its user model and to adapt the explanation.