[20] Josef Urban - Deductive and Inductive Reasoning in Large Libraries of Formalized Mathematics
The Thesis Review
Automating Theorem Proving: Advances in Premise Selection
This chapter investigates premise selection for automated theorem proving, revealing that approximately 40% of problems can be solved automatically. It examines the evolution of methods from traditional approaches to modern machine learning techniques, including deep neural networks, that enhance relevance and consistency in premise selection. The discussion underscores the importance of addressing data limitations and improving the adaptability of machine learning systems in mathematical reasoning.
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