Bibliographic Details
| Title: |
TacticToe: Learning to Prove with Tactics. |
| Authors: |
Gauthier, Thibault1 thibault.gauthier@uibk.ac.at, Kaliszyk, Cezary1, Urban, Josef2, Kumar, Ramana3, Norrish, Michael3 |
| Source: |
Journal of Automated Reasoning. Feb2021, Vol. 65 Issue 2, p257-286. 30p. |
| Subjects: |
Monte Carlo method, Machine learning, Mathematics theorems, Artificial intelligence, Automatic theorem proving |
| Abstract: |
We implement an automated tactical prover TacticToe on top of the HOL4 interactive theorem prover. TacticToe learns from human proofs which mathematical technique is suitable in each proof situation. This knowledge is then used in a Monte Carlo tree search algorithm to explore promising tactic-level proof paths. On a single CPU, with a time limit of 60 s, TacticToe proves 66.4% of the 7164 theorems in HOL4's standard library, whereas E prover with auto-schedule solves 34.5%. The success rate rises to 69.0% by combining the results of TacticToe and E prover. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Automated Reasoning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |
