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.

翻译：本文提出了一种基于知识表示与推理的城市空中交通管理（UATM）绕行管理新方法。该方法旨在理解城市空中交通（UAM）绕行的复杂性与需求，通过精心采样的环境快速识别安全高效的航线。该方案基于回答集编程实现，采用非单调推理机制，通过人类管理员与UATM系统间的两阶段对话，综合考虑安全性及潜在影响等因素。通过两个仿真场景的多次查询验证了方法的鲁棒性和有效性，有助于实现人类知识与先进人工智能技术的协同共生。本文包含引言（附相关研究引用）、问题建模、解决方案、讨论及结论等章节。