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Hammer for Coq: Automation for Dependent Type Theory

Overview of attention for article published in Journal of Automated Reasoning, February 2018
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  • Among the highest-scoring outputs from this source (#14 of 140)
  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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Title
Hammer for Coq: Automation for Dependent Type Theory
Published in
Journal of Automated Reasoning, February 2018
DOI 10.1007/s10817-018-9458-4
Pubmed ID
Authors

Łukasz Czajka, Cezary Kaliszyk

Abstract

Hammers provide most powerful general purpose automation for proof assistants based on HOL and set theory today. Despite the gaining popularity of the more advanced versions of type theory, such as those based on the Calculus of Inductive Constructions, the construction of hammers for such foundations has been hindered so far by the lack of translation and reconstruction components. In this paper, we present an architecture of a full hammer for dependent type theory together with its implementation for the Coq proof assistant. A key component of the hammer is a proposed translation from the Calculus of Inductive Constructions, with certain extensions introduced by Coq, to untyped first-order logic. The translation is "sufficiently" sound and complete to be of practical use for automated theorem provers. We also introduce a proof reconstruction mechanism based on an eauto-type algorithm combined with limited rewriting, congruence closure and some forward reasoning. The algorithm is able to re-prove in the Coq logic most of the theorems established by the ATPs. Together with machine-learning based selection of relevant premises this constitutes a full hammer system. The performance of the whole procedure is evaluated in a bootstrapping scenario emulating the development of the Coq standard library. For each theorem in the library only the previous theorems and proofs can be used. We show that 40.8% of the theorems can be proved in a push-button mode in about 40 s of real time on a 8-CPU system.

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 22%
Professor 3 11%
Student > Bachelor 3 11%
Student > Doctoral Student 2 7%
Researcher 2 7%
Other 4 15%
Unknown 7 26%
Readers by discipline Count As %
Computer Science 14 52%
Arts and Humanities 1 4%
Mathematics 1 4%
Unspecified 1 4%
Business, Management and Accounting 1 4%
Other 1 4%
Unknown 8 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 January 2021.
All research outputs
#7,774,029
of 24,325,299 outputs
Outputs from Journal of Automated Reasoning
#14
of 140 outputs
Outputs of similar age
#128,521
of 333,807 outputs
Outputs of similar age from Journal of Automated Reasoning
#2
of 5 outputs
Altmetric has tracked 24,325,299 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 140 research outputs from this source. They receive a mean Attention Score of 2.6. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,807 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.