Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration
It addresses route alteration challenges for urban air traffic management, representing an incremental improvement in integrating human supervision with AI systems.
This paper tackled the problem of managing detours in Urban Air Traffic Management by developing a method using knowledge representation and reasoning to quickly identify safe and efficient routes, validated through simulation scenarios.
This paper introduces a novel approach to detour management in Urban Air Traffic Management (UATM) using knowledge representation and reasoning. It aims to understand the complexities and requirements of UAM detours, enabling a method that quickly identifies safe and efficient routes in a carefully sampled environment. This method implemented in Answer Set Programming uses non-monotonic reasoning and a two-phase conversation between a human manager and the UATM system, considering factors like safety and potential impacts. The robustness and efficacy of the proposed method were validated through several queries from two simulation scenarios, contributing to the symbiosis of human knowledge and advanced AI techniques. The paper provides an introduction, citing relevant studies, problem formulation, solution, discussions, and concluding comments.