# Intent-Driven UAM Rescheduling

arXiv: https://arxiv.org/abs/2512.15462 · HTML: https://mlsanity.com/paper/2512.15462/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models](https://mlsanity.com/paper/2109.01401/) (2021)
- [Declarative Learning-Based Programming as an Interface to AI Systems](https://mlsanity.com/paper/1906.07809/) (2019)
- [Agent 3, change your route: possible conversation between a human manager and UAM Air Traffic Management (UATM)](https://mlsanity.com/paper/2306.14216/) (2023)
- [We, Vertiport 6, are temporarily closed: Interactional Ontological Methods for Changing the Destination](https://mlsanity.com/paper/2307.03558/) (2023)
- [Preference at First Sight](https://mlsanity.com/paper/1606.07524/) (2016)
- [Capacity-Constrained Urban Air Mobility Scheduling](https://mlsanity.com/paper/2107.02900/) (2021)
- [Dialogue Possibilities between a Human Supervisor and UAM Air Traffic Management: Route Alteration](https://mlsanity.com/paper/2308.06411/) (2023)
- [ASP-based Multi-shot Reasoning via DLV2 with Incremental Grounding](https://mlsanity.com/paper/2412.17143/) (2024)

## Abstract (arXiv)

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.