Neuro-Symbolic Agentic AI for Networked Low-Altitude UAVs
Networked low-altitude unmanned aerial vehicles (UAVs) need reliable and adaptive decision-making capabilities to operate under uncertain observations, dynamic environments, and intermittent connectivity, while many existing agentic systems remain limited by hallucination risks, data dependence, and weak generalization. This article investigates neuro-symbolic agentic AI (NSAAI) as a framework for combining neural grounding, symbolic reasoning, and closed-loop agentic interaction to support more reliable and adaptive UAV autonomy. We first examine its capability foundations in data efficiency, compositional generalization, continual learning, and zero-shot transfer, and then develop a ref...
| Authors | Yuqi Ping, Tianhao Liang, Nanchi Su et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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