Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Использование генеративного искусственного интеллекта для масштабирования интеллектуальных обучающих систем
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, Urban, Josef, Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra, Kaliszyk, Cezary, Olšák, Mirek, Schulz, Stephan, Suda, Martin, Piotrowski, Bartosz, Novickis, Alexander
2023-01-01

E and Vampire proversENIGMA and DeepireMizar theorem provingautomated theorem provinglearning-based premise selection
As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60% of the Mizar theorems in the hammer setting. We also automatically prove 75% of the Mizar theorems when the automated provers are helped by using only the premises used in the human-written Mizar proofs. We describe the methods and large-scale experiments leading to these results. This includes in particular the E and Vampire provers, their ENIGMA and Deepire learning modifications, a number of learning-based premise selection methods, and the incremental loop that interleaves growing a corpus of millions of ATP proofs with training increasingly strong AI/TP systems on them. We also present a selection of Mizar problems that were proved automatically.
1
An AI/automated-theorem-proving system automatically proves approximately 60% of Mizar theorems in the hammer setting.
2
An incremental training loop scales performance by generating millions of ATP proofs and repeatedly training stronger AI/theorem-proving systems on them.
3
Restricting automated provers to premises used in human-written Mizar proofs raises the automatically proved proportion to 75%.
4
The study reports large-scale experiments and provides a selection of Mizar problems proved automatically.
5
The system combines E and Vampire provers with ENIGMA and Deepire learning modifications and multiple learning-based premise-selection methods.

Mizar mathematical library theorems (Mizar corpus) being targeted by automated theorem proving

the scalability and proving performance of AI-assisted automated theorem proving, including premise selection and iterative learning from large ATP-proof corpora

Publication Details
Publication Date
2023-01-01
Journal
Publisher
ISSN
Cited by
79061
Access Type
Author Information
Authors
Ashish Vaswani
Noam Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan N. Gomez
Łukasz Kaiser
Illia Polosukhin
Urban, Josef
Jakubův, Jan
Chvalovský, Karel
Goertzel, Zarathustra
Kaliszyk, Cezary
Olšák, Mirek
Schulz, Stephan
Suda, Martin
Piotrowski, Bartosz
Novickis, Alexander
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat →
Make a presentation
100%