Graph Representations for Higher-Order Logic and Theorem Proving

24 May 2019  ·  Aditya Paliwal, Sarah Loos, Markus Rabe, Kshitij Bansal, Christian Szegedy ·

This paper presents the first use of graph neural networks (GNNs) for higher-order proof search and demonstrates that GNNs can improve upon state-of-the-art results in this domain. Interactive, higher-order theorem provers allow for the formalization of most mathematical theories and have been shown to pose a significant challenge for deep learning... Higher-order logic is highly expressive and, even though it is well-structured with a clearly defined grammar and semantics, there still remains no well-established method to convert formulas into graph-based representations. In this paper, we consider several graphical representations of higher-order logic and evaluate them against the HOList benchmark for higher-order theorem proving. read more

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Automated Theorem Proving HOList benchmark 4-hop GNN, sub-expression sharing Percentage correct 49.95 # 1

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