@book{SWP-2009-01,
year={2009},
author={Schiller, Marvin
    and Benzm{\"u}ller, Christoph},
title={Granularity-Adaptive Proof Presentation},
note={{\url{http://arxiv.org/abs/0903.0314}}},
series={{SEKI Working-Paper SWP--2009--01 (ISSN 1860--5931)}},
abstract={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 that exploits machine learning techniques to extract a model of the specific granularity of proof examples and employs this model for the automated generation of further proofs at an adapted level of granularity.},
keywords={Adaptive proof presentation, proof tutoring, automated reasoning, machine learning, granularity},
publisher={{SEKI Publications}},
address={{DFKI Bremen GmbH, Safe and Secure Cognitive Systems, Cartesium, Enrique Schmidt Str.\,5, D--28359 Bremen, Germany}},
}