Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Survey

TL;DR

This survey explores how Neurosymbolic AI can enhance Advanced Air Mobility by addressing complex challenges, reviewing current applications, and outlining future research directions for reliable and transparent systems.

Contribution

It provides a comprehensive classification of Neurosymbolic AI applications in AAM, highlighting current advancements, challenges, and future research pathways.

Findings

Neurosymbolic AI shows potential in demand forecasting and aircraft design.

Methodologies like Neurosymbolic Reinforcement Learning are promising but face scalability issues.

Research landscape is fragmented with hurdles in robustness and standards compliance.

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.