In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in tactic-based theorem proving.
We introduce a system called GamePad to explore the application of machine learning methods to theorem proving in the Coq proof assistant. This paper describes a system for applying machine learning to interactive theorem proving, focuses on tasks of tactic prediction and position evaluation, and shows that a neural model outperforms an SVM on both tasks. Proposes that machine learning techniques be used to help build proof in the theorem prover Coq.