Inductive Learning of Complex Knowledge from Raw Data

One of the ultimate goals of Artificial Intelligence is to learn generalised and human interpretable knowledge from raw data. Existing neuro-symbolic approaches partly tackle this problem by using manually engineered symbolic knowledge to improve the training of a neural network. In the few cases where symbolic knowledge is learned from raw data, this knowledge lacks the expressivity required to solve complex problems. In this paper, we introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a neural network to extract latent concepts from raw data, whilst learning symbolic knowledge that solves complex problems, defined in terms of these latent concepts. The novelty of our approach is a method for biasing a symbolic learner to learn improved knowledge, based on the in-training performance of both neural and symbolic components. We evaluate NSIL on two problem domains that require learning knowledge with different levels of complexity. Our experimental results demonstrate that NSIL learns knowledge of increased expressivity than what can be learned by the closest neuro-symbolic baseline systems, whilst outperforming them and other pure differentiable baseline models in terms of accuracy and data efficiency.