Holophrasm: a neural Automated Theorem Prover for higher-order logic
I propose a system for Automated Theorem Proving in higher order logic using deep learning and eschewing hand-constructed features. Holophrasm exploits the formalism of the Metamath language and explores partial proof trees using a neural-network-augmented bandit algorithm and a sequence-to-sequence model for action enumeration. The system proves 14% of its test theorems from Metamath's set.mm module.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Automated Theorem Proving | Metamath set.mm | Holophrasm | Percentage correct | 14.3 | # 3 |
Methods
No methods listed for this paper. Add
relevant methods here
