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Automated Reasoning and AI for Large Formal Mathematics

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If you have a question about this talk, please contact Louise Segar .

The first half of this talk will summarize several AI methods for learning and reasoning developed over large formal math corpora. We will show examples of AI systems implementing positive feedback loops between learning and deduction, and show the performance of the current methods over the Flyspeck, Isabelle, and Mizar libraries. The second half will discuss AI methods that we have recently started to develop for automating the translation of informal mathematics to formal. These methods combine statistical parsing of informal mathematics with the large-theory theorem proving methods.

This talk is part of the Machine Learning @ CUED series.

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