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.
View on arXiv@article{jacob2025_2506.14577,
title={ Object-Centric Neuro-Argumentative Learning },
author={ Abdul Rahman Jacob and Avinash Kori and Emanuele De Angelis and Ben Glocker and Maurizio Proietti and Francesca Toni },
journal={arXiv preprint arXiv:2506.14577},
year={ 2025 }
}