Proceedings

Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey

Kamal Acharya, Iman Sharifi, Mehul Lad, Liang Sun, Houbing Song

Download BibTeX Cite

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Survey Track. Pages 10362-10370.

https://doi.org/10.24963/ijcai.2025/1151

Keywords

  • Machine Learning: ML: Applications
  • Machine Learning: ML: Knowledge-aided learning
  • Multidisciplinary Topics and Applications: MTA: Transportation
  • Multidisciplinary Topics and Applications: General
  • Machine Learning: ML: Neuro-symbolic methods

Abstract

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.

Kamal Acharya, Iman Sharifi, Mehul Lad, Liang Sun, and Houbing Song. 2025. Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10362–10370. https://doi.org/10.24963/ijcai.2025/1151
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards