Abstract

Neuro-symbolic Artificial Intelligence integrates the learning capabilities of neural networks with the interpretability and logical rigor of symbolic reasoning. This hybrid approach seeks to create more robust, generalizable, and explainable Artificial Intelligence (AI) systems, directly addressing the limitations of purely neural or symbolic methods. It has wide-ranging applications, from enhancing diagnostic accuracy in medical image analysis and building ethical decision-making systems to improving concept learning and efficiently processing structured data. Furthermore, it contributes significantly to explainable AI, combinatorial optimization, and computer vision, aiming to achieve richer semantic understanding and trustworthy intelligent systems that can articulate their reasoning.