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Machine Learning for Inductive Theorem Proving
[chapter]
2018
Lecture Notes in Computer Science
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Over the past few years, machine learning has been successfully combined with automated theorem provers to prove conjectures from proof assistants. However, such approaches do not usually focus on inductive proofs. In this work, we explore a combination of machine learning, a simple Boyer-Moore model and ATPs as a means of improving the automation of inductive proofs in the proof assistant HOL Light. We evaluate the framework using a number of inductive proof corpora. In each case, our approach
doi:10.1007/978-3-319-99957-9_6
fatcat:bexvmw6k4bezfpji74hfv3evhi