Tactic Learning and Proving for the Coq Proof Assistant
arXiv:2003.09140 · doi:10.29007/wg1q
Abstract
We present a system that utilizes machine learning for tactic proof search in the Coq Proof Assistant. In a similar vein as the TacticToe project for HOL4, our system predicts appropriate tactics and finds proofs in the form of tactic scripts. To do this, it learns from previous tactic scripts and how they are applied to proof states. The performance of the system is evaluated on the Coq Standard Library. Currently, our predictor can identify the correct tactic to be applied to a proof state 23.4% of the time. Our proof searcher can fully automatically prove 39.3% of the lemmas. When combined with the CoqHammer system, the two systems together prove 56.7% of the library's lemmas.
arXiv comment12 pages, 2 figures, 1 table. For the associated artefacts, see https://doi.org/10.5281/zenodo.3693760
References in corpus (7)
- Learning-Assisted Automated Reasoning with Flyspeck
- MizAR 40 for Mizar 40
- GamePad: A Learning Environment for Theorem Proving
- Machine Learning in Proof General: Interfacing Interfaces
- Learning to Prove Theorems via Interacting with Proof Assistants
- Property Invariant Embedding for Automated Reasoning
- Reinforcement Learning of Theorem Proving