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Applications of Machine Learning in Automated Theorem Proving

Student: Roman Shchedrin

Supervisor: Artem Babenko

Faculty: Faculty of Computer Science

Educational Programme: Data Science (Master)

Year of Graduation: 2020

The goal of this work is to leverage machine learning methods to speed up automated theorem proving. In this work we improve an existing ATP system Proverbot9001 by upgrading its search algorithm from neural-guided Depth First Search, to neural-guided Best First Search. We also introduce useful heuristics that make this transition doable.

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