Schubert's steamroller problem: formulations and solutions
Journal of Automated Reasoning
The effect of resource limits and task complexity on collaborative planning in dialogue
Artificial Intelligence - Special volume on empirical methods
Verbalization of high-level formal proofs
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
International Journal of Human-Computer Studies - Special issue on collaboration, cooperation and conflict in dialogue systems
TLCA '95 Proceedings of the Second International Conference on Typed Lambda Calculi and Applications
Reconstruction Proofs at the Assertion Level
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
Integration of Automated and Interactive Theorem Proving in ILP
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
Transforming Matings into Natural Deduction Proofs
Proceedings of the 5th Conference on Automated Deduction
Proof verbalization as an application of NLG
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Using a cognitive architecture to plan dialogs for the adaptive explanation of proofs
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Generation of biomedical arguments for lay readers
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
A Framework to Extract Arguments in Opinion Texts
International Journal of Cognitive Informatics and Natural Intelligence
A Framework to Extract Arguments in Opinion Texts
International Journal of Cognitive Informatics and Natural Intelligence
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Explaining solutions to logical problems is one of the areas where argumentation in natural language plays a prominent role. One crucial reason for the difficulty in pursuing this issue in a systematic manner when relying on formal inference systems lies in the discrepancy between machine-oriented reasoning and human-adequate argumentation. Aiming at bridging these two divergent views, we present a model for producing human-adequate argumentation from machine-oriented inference structures. Ingredients of our method are techniques to build representations to suitable degrees of abstraction and explicitness, and a module for their interactive and adaptive exploration. The presented techniques are not only relevant for the interactive use of theorem provers, but they also have the potential to support the functionality of dialog-oriented tutorial systems.