Graph sequence learning for premise selection

Abstract

Premise selection is crucial for large theory reasoning with automated theorem provers as the sheer size of the problems quickly leads to resource exhaustion. This paper proposes a premise selection method inspired by the machine learning domain of image captioning, where language models automatically generate a suitable caption for a given image. Likewise, we attempt to generate the sequence of axioms required to construct the proof of a given conjecture. In our axiom captioning approach, a pre-trained graph neural network is combined with a language model via transfer learning to encapsulate both the inter-axiom and conjecture-axiom relationships. We evaluate different configurations of our method and experience a 14% improvement in the number of solved problems over a baseline.