With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data. In order to evaluate and analyse the architecture, we introduce a family of simple visual relational reasoning tasks of varying complexity. We show that the proposed architecture, when pre-trained on a curriculum of such tasks, learns to generate reusable representations that better facilitate subsequent learning on previously unseen tasks when compared to a number of baseline architectures. The workings of a successfully trained model are visualised to shed some light on how the architecture functions.
@article{arxiv.1905.10307,
title = {An Explicitly Relational Neural Network Architecture},
author = {Murray Shanahan and Kyriacos Nikiforou and Antonia Creswell and Christos Kaplanis and David Barrett and Marta Garnelo},
journal= {arXiv preprint arXiv:1905.10307},
year = {2020}
}