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Neuro-Symbolic Hierarchical Rule Induction

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

Hall E #308

Keywords: [ RL: Deep RL ] [ SA: Accountability, Transparency and Interpretability ] [ DL: Other Representation Learning ]


We propose Neuro-Symbolic Hierarchical Rule Induction, an efficient interpretable neuro-symbolic model, to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a pre-defined set of meta-rules organized in a hierarchical structure, first-order rules are invented by learning embeddings to match facts and body predicates of a meta-rule. To instantiate, we specifically design an expressive set of generic meta-rules, and demonstrate they generate a consequent fragment of Horn clauses. As a differentiable model, HRI can be trained both via supervised learning and reinforcement learning. To converge to interpretable rules, we inject a controlled noise to avoid local optima and employ an interpretability-regularization term. We empirically validate our model on various tasks (ILP, visual genome, reinforcement learning) against relevant state-of-the-art methods, including traditional ILP methods and neuro-symbolic models.

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