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arXiv · 2112.13418

Neuro-Symbolic Hierarchical Rule Induction

Claire Glanois·Xuening Feng·Zhaohui Jiang·Paul Weng·Matthieu Zimmer·Dong Li·Wulong Liu
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Abstract

We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in a hierarchical structure, first-order rules are invented by learning embeddings to match facts and body predicates of a meta-rule. To instantiate it, we specifically design an expressive set of generic meta-rules, and demonstrate they generate a consequent fragment of Horn clauses. During training, we inject a controlled \pw{Gumbel} noise to avoid local optima and employ interpretability-regularization term to further guide the convergence to interpretable rules. We empirically validate our model on various tasks (ILP, visual genome, reinforcement learning) against several state-of-the-art methods.

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Claire Glanois, Xuening Feng, Zhaohui Jiang, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu. 2021-12-26. Neuro-Symbolic Hierarchical Rule Induction. https://arxiv.org/abs/2112.13418

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