Machine Learning in Proof General: Interfacing Interfaces

Ekaterina Komendantskaya
(School of Computing, University of Dundee)
Jónathan Heras
(School of Computing, University of Dundee)
Gudmund Grov
(School of Mathematical and Computer Sciences, Heriot-Watt University)

We present ML4PG - a machine learning extension for Proof General. It allows users to gather proof statistics related to shapes of goals, sequences of applied tactics, and proof tree structures from the libraries of interactive higher-order proofs written in Coq and SSReflect. The gathered data is clustered using the state-of-the-art machine learning algorithms available in MATLAB and Weka. ML4PG provides automated interfacing between Proof General and MATLAB/Weka. The results of clustering are used by ML4PG to provide proof hints in the process of interactive proof development.

In Cezary Kaliszyk and Christoph Lüth: Proceedings 10th International Workshop On User Interfaces for Theorem Provers (UITP 2012), Bremen, Germany, July 11th 2012, Electronic Proceedings in Theoretical Computer Science 118, pp. 15–41.
Published: 5th July 2013.

ArXived at: http://dx.doi.org/10.4204/EPTCS.118.2 bibtex PDF
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