Intent-Driven UAM Rescheduling
This work addresses the challenge of handling vague human inputs in UAM scheduling, which is an incremental improvement for the domain of urban air mobility operations.
The paper tackles the problem of dynamic and ambiguous rescheduling requests in Urban Air Mobility (UAM) by proposing an integrated system that combines Answer Set Programming (ASP) and Mixed Integer Linear Programming (MILP), resulting in a robust framework for explainable and adaptive scheduling.
Due to the restricted resources, efficient scheduling in vertiports has received much more attention in the field of Urban Air Mobility (UAM). For the scheduling problem, we utilize a Mixed Integer Linear Programming (MILP), which is often formulated in a resource-restricted project scheduling problem (RCPSP). In this paper, we show our approach to handle both dynamic operation requirements and vague rescheduling requests from humans. Particularly, we utilize a three-valued logic for interpreting ambiguous user intents and a decision tree, proposing a newly integrated system that combines Answer Set Programming (ASP) and MILP. This integrated framework optimizes schedules and supports human inputs transparently. With this system, we provide a robust structure for explainable, adaptive UAM scheduling.