Intent-Driven UAM Rescheduling

TL;DR

This paper presents an integrated framework combining Answer Set Programming and MILP to handle dynamic, ambiguous user requests for efficient, explainable scheduling in Urban Air Mobility vertiports.

Contribution

It introduces a novel system that interprets vague user intents using three-valued logic and decision trees, enhancing UAM scheduling adaptability and transparency.

Findings

Effective handling of ambiguous user requests.

Improved scheduling efficiency in resource-constrained environments.

Enhanced explainability of scheduling decisions.

Abstract

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.