C4.5: programs for machine learning
C4.5: programs for machine learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
P.rex: An Interactive Proof Explainer
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Natural language dialog with a tutor system for mathematical proofs
Proceedings of the 2005 joint Chinese-German conference on Cognitive systems
Textbook proofs meet formal logic: the problem of underspecification and granularity
MKM'05 Proceedings of the 4th international conference on Mathematical Knowledge Management
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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When mathematicians present proofs they usually adapt their explanations to their didactic goals and to the (assumed) knowledge of their addressees. Modern automated theorem provers, in contrast, present proofs usually at a fixed level of detail (also called granularity). Often these presentations are neither intended nor suitable for human use. A challenge therefore is to develop user- and goal-adaptive proof presentation techniques that obey common mathematical practice. We present a flexible and adaptive approach to proof presentation based on classification. Expert knowledge for the classification task can be hand-authored or extracted from annotated proof examples via machine learning techniques. The obtained models are employed for the automated generation of further proofs at an adapted level of granularity.