将神经符号AI用于空中交通管理，提升安全性与透明度。
Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey
- 融合神经网络与符号推理，应对复杂空管挑战
- 在需求预测与实时调度中展现优化潜力
- 适合航空系统研发与智能交通研究者参考
神经符号AI结合神经网络的适应性与符号推理能力，有望解决先进空中交通管理（AAM）中的监管、运营与安全难题。本综述系统分析其在需求预测、飞机设计及实时空管等关键领域的应用。研究发现当前进展分散，尽管神经符号强化学习在动态优化中展现出潜力，仍面临可扩展性、鲁棒性及适航标准合规性挑战。本文梳理现有成果，呈现典型案例，并提出未来研究方向，旨在推动此类技术融入可靠、透明的AAM系统。通过连接先进AI与实际空运需求，为下一代空中出行解决方案的研发提供清晰路径。
原文摘要 · Abstract (English)
Neurosymbolic AI combines neural network adaptability with symbolic reasoning, promising an approach to address the complex regulatory, operational, and safety challenges in Advanced Air Mobility (AAM). This survey reviews its applications across key AAM domains such as demand forecasting, aircraft design, and real-time air traffic management. Our analysis reveals a fragmented research landscape where methodologies, including Neurosymbolic Reinforcement Learning, have shown potential for dynamic optimization but still face hurdles in scalability, robustness, and compliance with aviation standards. We classify current advancements, present relevant case studies, and outline future research directions aimed at integrating these approaches into reliable, transparent AAM systems. By linking advanced AI techniques with AAM's operational demands, this work provides a concise roadmap for researchers and practitioners developing next-generation air mobility solutions.
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