ENIGMAWatch: ProofWatch Meets ENIGMA
This work addresses the challenge of enhancing automated theorem proving efficiency for researchers in formal verification, though it is incremental as it builds on existing methods.
The authors tackled the problem of improving proof guidance for first-order theorem provers by combining symbolic proof matching (ProofWatch) with statistical machine learning (ENIGMA), resulting in ENIGMAWatch, which showed performance improvements over both methods on problems from the Mizar Library.
In this work we describe a new learning-based proof guidance -- ENIGMAWatch -- for saturation-style first-order theorem provers. ENIGMAWatch combines two guiding approaches for the given-clause selection implemented for the E ATP system: ProofWatch and ENIGMA. ProofWatch is motivated by the watchlist (hints) method and based on symbolic matching of multiple related proofs, while ENIGMA is based on statistical machine learning. The two methods are combined by using the evolving information about symbolic proof matching as an additional information that characterizes the saturation-style proof search for the statistical learning methods. The new system is experimentally evaluated on a large set of problems from the Mizar Library. We show that the added proof-matching information is considered important by the statistical machine learners, and that it leads to improvements in E's Performance over ProofWatch and ENIGMA.