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Machine-Learned Premise Selection for Lean by Piotrowski, Bartosz; Mir, Ramon Fernández; Ayers, Edward is a scholarly article available to read on EtoBox.

What is Machine-Learned Premise Selection for Lean about?

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 suggest_premises tactic which can be called in an editor while constructing a proof interactively.

Author

Piotrowski, Bartosz; Mir, Ramon Fernández; Ayers, Edward

Published

2023

Language

EN