Abstract
UAVs are poised to play a pivotal role in next-generation communication networks, supporting applications from emergency coverage to large-scale IoT connectivity. However, enabling intelligent and autonomous UAV networking remains challenging because conventional Artificial Intelligence (AI) approaches have inherent limitations. Neural models provide adaptability but lack interpretability, while symbolic methods ensure explainability yet struggle with scalability in dynamic environments. This article examines the potential of Neuro-Symbolic Artificial Intelligence (NSAI) to bridge this gap by combining data-driven learning with logic-based reasoning. We outline the fundamentals of NSAI, explain its architectural integration into UAV communication systems across edge and cloud layers, and discuss its capacity to support interpretable, constraint-compliant decision-making in safety-critical missions. Representative use cases illustrate how NSAI can strengthen reliability, adaptability, and transparency in UAV operations. Finally, key challenges and open research directions, such as scalability, adversarial robustness, and alignment with Sixth-Generation (6G) and Non-Terrestrial Networks (NTN), are highlighted, offering a guideline for advancing NSAI as a pathway toward intelligent, resilient, and explainable aerial networks.