AI/ML

Bayesian Optimisation with Gaussian Processes for Premise Selection. (arXiv:1909.09137v1 [cs.AI])

Heuristics in theorem provers are often parameterised. Modern theorem provers
such as Vampire 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 probablistic
framework for heuristics optimisation in theorem provers. We present results
using a heuristic for premise selection and The Archive of Formal Proofs (AFP)
as a case study.

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