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

TL;DR

This paper presents a knowledge-based reasoning approach for UAM air traffic route detours, enabling safe, efficient, and human-in-the-loop route management through Answer Set Programming.

Contribution

It introduces a novel non-monotonic reasoning method for UAM detour management that integrates human supervisor input with AI techniques.

Findings

Validated through simulation scenarios

Effective in identifying safe detours

Enhances human-AI collaboration in UATM

Abstract

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.