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This paper introduces a novel approach to detour management in Urban AirTraffic Management (UATM) using knowledge representation and reasoning. It aimsto understand the complexities and requirements of UAM detours, enabling amethod that quickly identifies safe and efficient routes in a carefully sampledenvironment. This method implemented in Answer Set Programming usesnon-monotonic reasoning and a two-phase conversation between a human managerand the UATM system, considering factors like safety and potential impacts. Therobustness and efficacy of the proposed method were validated through severalqueries from two simulation scenarios, contributing to the symbiosis of humanknowledge and advanced AI techniques. The paper provides an introduction,citing relevant studies, problem formulation, solution, discussions, andconcluding comments.

Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration