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Proof Guidance in PVS with Sequential Pattern Mining

  • M. Saqib NawazEmail author
  • Meng Sun
  • Philippe Fournier-Viger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11761)

Abstract

The recent introduction of the big data paradigm and advancements in machine learning and deep mining techniques have made proof guidance and automation in interactive theorem provers (ITPs) an important research topic. In this paper, we provide a learning approach based on sequential pattern mining (SPM) for proof guidance in the PVS proof assistant. Proofs in a PVS theory are first abstracted to a computer-processable corpus. SPM techniques are then used on the corpus to discover frequent proof steps and proof patterns, relationships of proof steps / patterns with each other, dependency of new conjectures on already proved facts and to predict the next proof step(s). Obtained results suggest that the integration of SPM in proof assistants can be used to guide the proof process and in the development of proof tactics/strategies.

Keywords

PVS Proof development process Proof corpus Frequent patterns Sequential pattern mining 

Notes

Acknowledgement

The work has been supported by the National Natural Science Foundation of China under grant no. 61772038, 61532019 and 61272160, and the Guandong Science and Technology Department (Grant no. 2018B010107004).

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • M. Saqib Nawaz
    • 1
    Email author
  • Meng Sun
    • 1
  • Philippe Fournier-Viger
    • 2
  1. 1.LMAM and Department of Informatics, School of Mathematical SciencesPeking UniversityBeijingChina
  2. 2.School of Humanities and Social SciencesHarbin Institute of Technology (Shenzhen)ShenzhenChina

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