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Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
arXiv - CS - Logic in Computer Science Pub Date : 2019-11-15 , DOI: arxiv-1911.06904
Maxwell Crouse; Ibrahim Abdelaziz; Cristina Cornelio; Veronika Thost; Lingfei Wu; Kenneth Forbus; Achille Fokoue

Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems. In particular, there has been a growing interest in adapting structure-aware neural methods to work with the underlying graph representations of logical expressions. While more effective than character and token-level approaches, such methods have often made representational trade-offs that limited their ability to capture key structural properties of their inputs. In this work we propose a novel, LSTM-based approach for embedding logical formulae that is designed to overcome the representational limitations of prior approaches. Our proposed architecture works for logics of different expressivity; e.g., first-order and higher-order logic. We evaluate our approach on two standard datasets and show that the proposed architecture improves the performance of premise selection and proof step classification significantly compared to state-of-the-art.
更新日期:2020-02-13

 

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