Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Использование генеративного искусственного интеллекта для масштабирования интеллектуальных обучающих систем
2023-01-01
SCID: 54.1/25jm2dne
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E and Vampire proversENIGMA and DeepireMizar theorem provingautomated theorem provinglearning-based premise selection
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Abstract (AI)
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.
Key Findings
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.
Research Object
Mizar mathematical library theorems (Mizar corpus) being targeted by automated theorem proving
Research Subject
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
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