Machine-Learned Premise Selection for Lean
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F23%3A00372256" target="_blank" >RIV/68407700:21730/23:00372256 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-43513-3_10" target="_blank" >https://doi.org/10.1007/978-3-031-43513-3_10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-43513-3_10" target="_blank" >10.1007/978-3-031-43513-3_10</a>
Alternative languages
Result language
angličtina
Original language name
Machine-Learned Premise Selection for Lean
Original language description
We introduce a machine-learning-based tool for the Lean proof assistant that suggests relevant premises for theorems being proved by a user. The design principles for the tool are (1) tight integration with the proof assistant, (2) ease of use and installation, (3) a lightweight and fast approach. For this purpose, we designed a custom version of the random forest model, trained in an online fashion. It is implemented directly in Lean, which was possible thanks to the rich and efficient metaprogramming features of Lean 4. The random forest is trained on data extracted from mathlib – Lean’s mathematics library. We experiment with various options for producing training features and labels. The advice from a trained model is accessible to the user via the tactic which can be called in an editor while constructing a proof interactively.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Automated Reasoning with Analytic Tableaux and Related Methods
ISBN
978-3-031-43512-6
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
12
Pages from-to
175-186
Publisher name
Springer, Cham
Place of publication
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Event location
Praha
Event date
Sep 18, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001162233100010