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Holophrasm: a neural Automated Theorem Prover for higher-order logic

About

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.

Daniel Whalen• 2016

Related benchmarks

TaskDatasetResultRank
Theorem Provingset.mm (test)
Proofs Found (Test)557
14
Theorem Proving (Relevance Ranking)set.mm (val)
Top-1 Accuracy51.52
12
Theorem Proving (Substitution Network)set.mm (val)
Prob0.6142
12
Theorem Provingiset.mm (test)
Proofs Found378
2
Theorem Proving (Relevance Ranking)iset.mm (val)
Top-1 Accuracy43.27
2
Theorem Proving (Substitution Network)iset.mm (val)
Probability0.1723
2
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