面向网络化低空无人机的神经符号智能体AI
Neuro-Symbolic Agentic AI for Networked Low-Altitude UAVs
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中文总结 AI 辅助
本文提出神经符号智能体AI框架，结合神经基础与符号推理，增强网络化低空无人机在不确定环境下的决策可靠性，并通过城市消防巡检案例验证其可重用技能与证据驱动决策的潜力。
中文摘要 AI 辅助
网络化低空无人机（UAV）在不确定观测、动态环境和间歇性连接条件下运行，需要可靠且自适应的决策能力，而许多现有的智能体系统仍受限于幻觉风险、数据依赖和泛化能力弱等问题。本文研究神经符号智能体AI（NSAAI）作为结合神经基础、符号推理和闭环智能体交互的框架，以支持更可靠和自适应的无人机自主性。我们首先考察其在数据效率、组合泛化、持续学习和零样本迁移方面的能力基础，然后开发一个参考架构，整合任务与目标管理、神经符号规划、验证与元认知、技能执行与网络交互，以及共享知识与记忆。在LAESim中实现的城市消防巡检案例展示了无人机如何在间歇性连接下协调感知与云访问，重用经过验证的图像传递技能，并在完成任务前满足明确的证据条件。结果展示了NSAAI在网络化无人机系统中支持可重用技能、基于证据的决策和自适应任务执行的潜力。我们进一步讨论了不确定性感知推理、知识与技能扩展、自适应自我监控和标准化评估等关键研究方向。
英文摘要
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 reference architecture integrating task and goal management, neuro-symbolic planning, verification and metacognition, skill execution and network interaction, and shared knowledge and memory. An urban fire-inspection case implemented in LAESim illustrates how a UAV can coordinate sensing and cloud access under intermittent connectivity, reuse a verified image-delivery skill, and satisfy explicit evidence conditions before completing the mission. The results illustrate the potential of NSAAI to support reusable skills, evidence-grounded decision-making, and adaptive mission execution in networked UAV systems. We further discuss key research directions in uncertainty-aware reasoning, knowledge and skill expansion, adaptive self-monitoring, and standardized evaluation.
发表机构
- Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。
