Name
Abstract | ||
|---|---|---|
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 at a human level of abstraction. 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 human-level theorem proving. |
Year | Venue | Field |
|---|---|---|
2018 | international conference on learning representations | Theorem provers,Algebraic number,Automated theorem proving,Mathematical proof,Artificial intelligence,Learning environment,Calculus,Machine learning,Mathematics,Proof assistant |
DocType | Volume | Citations |
Journal | abs/1806.00608 | 4 |
PageRank | References | Authors |
0.38 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| daniel c huang | 1 | 8 | 1.97 |
| Prafulla Dhariwal | 2 | 4 | 0.38 |
| Dawn Song | 3 | 7334 | 385.37 |
| Ilya Sutskever | 4 | 25814 | 1120.24 |