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arXiv · 1108.3446

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Jesse Alama·Tom Heskes·Daniel Kühlwein·Evgeni Tsivtsivadze·Josef Urban
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Abstract

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.

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Jesse Alama, Tom Heskes, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban. 2011-08-17. Premise Selection for Mathematics by Corpus Analysis and Kernel Methods. https://doi.org/10.1007/s10817-013-9286-5

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