Object-Centric Neuro-Argumentative Learning

Over the last decade, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and interpretability have emerged. We introduce a novel Neural Argumentative Learning (NAL) architecture that integrates Assumption-Based Argumentation (ABA) with Object-Centric (OC) deep learning for image analysis. Our OC-NAL architecture consists of neural and symbolic components. The former segments and encodes images into facts, while the latter applies ABA learning to develop ABA frameworks enabling image classification. Experiments on synthetic data show that the OC-NAL architecture can be competitive with a state-of-the-art alternative. The code can be found at https://github.com/AbdulRJacob/Neuro-AL