Neural-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes

The predicates used for Inductive Logic Programming (ILP) systems are usually elusive and need to be hand-crafted in advance, which limits the generalization of the system when learning new rules without sufficient background knowledge. Predicate Invention (PI) for ILP is the problem of discovering new concepts that describe hidden relationships in the domain. PI can mitigate the generalization problem for ILP by inferring new concepts, giving the system a better vocabulary to compose logic ruless. Although there are several PI approaches for symbolic ILP systems, PI for NeSy ILP systems that can handle visual input to learn logical rules using differentiable reasoning is relatively unaddressed. To this end, we propose a neural-symbolic approach, NeSy-, to invent predicates from visual scenes for NeSy ILP systems based on clustering and extension of relational concepts. ( denotes the abbrivation of Predicate Invention). NeSy-π processes visual scenes as input using deep neural networks for the visual perception and invents new concepts that support the task of classifying complex visual scenes. The invented concepts can be used by any NeSy ILP systems instead of hand-crafted background knowledge. Our experiments show that the PI model is capable of inventing high-level concepts and solving complex visual logic patterns more efficiently and accurately in the absence of explicit background knowledge. Moreover, the invented concepts are explainable and interpretable, while also providing competitive results with state-of-the-art NeSy ILP systems based on given knowledge.