Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract)
Agnieszka Słowik, Chaitanya Mangla, Mateja Jamnik, Sean B. Holden, Lawrence Charles Paulson · Proceedings of the AAAI Conference on Artificial Intelligence · 2020
Modern theorem provers utilise a wide array of heuristics to control the search space explosion, thereby requiring optimisation of a large set of parameters. An exhaustive search in this multi-dimensional parameter space is intractable in most cases, yet the performance of the provers is highly dependent on the parameter assignment. In this work, we introduce a principled probabilistic framework for heuristic optimisation in theorem provers. We present results using a heuristic for premise selection and the Archive of Formal Proofs (AFP) as a case study.