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International Conference on Swarm Intelligence

ICSI 2021: Advances in Swarm Intelligence pp 253–262Cite as

Proof Searching in PVS Theorem Prover Using Simulated Annealing

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12690))

Abstract

The proof development process in PVS theorem prover is interactive in nature, that is not only laborious but consumes lots of time. For proof searching and optimization in PVS, a heuristic proof searching approach is provided where simulated annealing (SA) is used to search and optimize the proofs for formalized theorems/lemmas in PVS theories. In the proposed approach, random proof sequence is first generated from a population of frequently occurring PVS proof steps that are discovered with sequential pattern mining. Generated proof sequence then goes through the annealing process till its fitness matches with the fitness of the target proof sequence. Moreover, the performance of SA with a genetic algorithm (GA) is compared. Obtained results suggest that evolutionary/heuristic techniques can be combined with proof assistants to efficiently support proofs finding and optimization.

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Author information

Authors and Affiliations

  1. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    M. Saqib Nawaz & Philippe Fournier-Viger

  2. School of Mathematical Sciences, Peking University, Beijing, China

    Meng Sun

Authors
  1. M. Saqib Nawaz
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  2. Meng Sun
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  3. Philippe Fournier-Viger
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Corresponding author

Correspondence to Philippe Fournier-Viger .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Nawaz, M.S., Sun, M., Fournier-Viger, P. (2021). Proof Searching in PVS Theorem Prover Using Simulated Annealing. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_24

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  • DOI: https://doi.org/10.1007/978-3-030-78811-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78810-0

  • Online ISBN: 978-3-030-78811-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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