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Machine Learning for Inductive Theorem Proving

  • Yaqing JiangEmail author
  • Petros Papapanagiotou
  • Jacques Fleuriot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11110)

Abstract

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 achieves a higher success rate than running ATPs or the Boyer-Moore tool individually.

Keywords

Induction Lemma selection Theorem proving Machine learning 

Notes

Acknowledgements

This research was supported by EPSRC grants: ProofPeer: Collaborative Theorem Proving EP/L011794/1 and The Integration and Interaction of Multiple Mathematical Reasoning Processes EP/N014758/1. It was also supported by the China Scholarship Council (CSC).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yaqing Jiang
    • 1
    Email author
  • Petros Papapanagiotou
    • 1
  • Jacques Fleuriot
    • 1
  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK

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