2025/06/17 by Jacob, Abdul Rahman, Avinash Kori, Emanuele De Angelis +7 · 1 citation
Social Sciences · #Education and Critical Thinking Development
paper · pdf · doi:10.48550/arxiv.2506.14577
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 deep learning for image analysis. Our architecture consists of neural and symbolic components. The former segments and encodes images into facts using object-centric learning, while the latter applies ABA learning to develop ABA frameworks enabling predictions with images. Experiments on synthetic data show that the NAL architecture can be competitive with a state-of-the-art alternative.