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CoProver: A Recommender System for Proof Construction

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Intelligent Computer Mathematics (CICM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14101))

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Abstract

Interactive Theorem Provers (ITPs) are an indispensable tool in the arsenal of formal method experts as a platform for construction and (formal) verification of proofs. The complexity of the proofs in conjunction with the level of expertise typically required for the process to succeed can often hinder the adoption of ITPs. A recent strain of work has investigated methods to incorporate machine learning models trained on ITP user activity traces as a viable path towards full automation. While a valuable line of investigation, many problems still require human supervision to be completed fully, thus applying learning methods to assist the user with useful recommendations can prove more fruitful. Following the vein of user assistance, we introduce CoProver, a proof recommender system based on transformers, capable of learning from past actions during proof construction, all while exploring knowledge stored in the ITP concerning previous proofs. CoProver employs a neurally learnt sequence-based encoding of sequents, capturing long distance relationships between terms and hidden cues therein. We couple CoProver with the Prototype Verification System (PVS) and evaluate its performance on two key areas, namely: (1) Next Proof Action Recommendation, and (2) Relevant Lemma Retrieval given a library of theories. We evaluate CoProver on a series of well-established metrics originating from the recommender system and information retrieval communities, respectively. We show that CoProver successfully outperforms prior state of the art applied to recommendation in the domain. We conclude by discussing future directions viable for CoProver (and similar approaches) such as argument prediction, proof summarization, and more.

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Notes

  1. 1.

    https://shemesh.larc.nasa.gov/fm/pvs/PVS-library/.

  2. 2.

    A step is a single forward-predict pass over a training instance, and multiple steps can be performed over the same data during the training phase.

  3. 3.

    Initial experiments with larger samples showed no difference in performance with a system trained with the smaller set.

  4. 4.

    https://www.pytorchlightning.ai/.

  5. 5.

    https://scikit-learn.org/.

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Acknowledgement

This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR00112290064 and by the National Institute of Aeronautics. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Government or DARPA.

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Authors and Affiliations

  1. SRI International, Menlo Park, CA, 94025, USA

    Eric Yeh, Briland Hitaj, Sam Owre, Maena Quemener & Natarajan Shankar

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  1. Eric Yeh
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Correspondence to Eric Yeh .

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  1. ENSIIE, Evry, France

    Catherine Dubois

  2. University of Birmingham, Birmingham, UK

    Manfred Kerber

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Yeh, E., Hitaj, B., Owre, S., Quemener, M., Shankar, N. (2023). CoProver: A Recommender System for Proof Construction. In: Dubois, C., Kerber, M. (eds) Intelligent Computer Mathematics. CICM 2023. Lecture Notes in Computer Science(), vol 14101. Springer, Cham. https://doi.org/10.1007/978-3-031-42753-4_16

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