The Isabelle ENIGMA
This work provides significant performance gains for automated theorem proving in the Isabelle proof assistant, though it appears to be an incremental improvement on existing methods.
The authors tackled the problem of improving automated theorem proving performance on Isabelle Sledgehammer problems by combining learning and theorem proving methods, achieving a 25.3% improvement over the previous best version of the E prover in 15 seconds.
We significantly improve the performance of the E automated theorem prover on the Isabelle Sledgehammer problems by combining learning and theorem proving in several ways. In particular, we develop targeted versions of the ENIGMA guidance for the Isabelle problems, targeted versions of neural premise selection, and targeted strategies for E. The methods are trained in several iterations over hundreds of thousands untyped and typed first-order problems extracted from Isabelle. Our final best single-strategy ENIGMA and premise selection system improves the best previous version of E by 25.3% in 15 seconds, outperforming also all other previous ATP and SMT systems.