Unified theories of cognition
Generating explanatory discourse
Current research in natural language generation
The role of the user's domain knowledge in generation
Computational Intelligence
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
Omega: Towards a Mathematical Assistant
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Proof verbalization as an application of NLG
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Argumentation in Explanations to Logical Problems
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
AISC '00 Revised Papers from the International Conference on Artificial Intelligence and Symbolic Computation
P.rex: An Interactive Proof Explainer
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
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In order to generate high quality explanations in technical or mathematical domains, the presentation must be adapted to the knowledge of the intended audience. Current proof presentation systems only communicate proofs on a fixed degree of abstraction independently of the addressee's knowledge. In this paper we propose an architecture for an interactive proof explanation system, called Prex. Based on the theory of human cognition ACT-R., its dialog planner exploits a cognitive model, in which both the user's knowledge and his cognitive processes are modeled. By this means, his cognitive states are traced during the explanation. The explicit representation of the user's cognitive states in ACT-R allows the dialog planner to choose a degree of abstraction tailored to the user for each proof step to be explained. Moreover, the system can revise its assumptions about the user's knowledge and react to his interactions.