Intent-Driven UAM Scheduling: An Explainable Hybrid AI Framework
Abstract
This paper presents a hybrid AI framework for rescheduling tasks within UAM vertiports. This scheduling challenge is approached as a resource-constrained project scheduling problem (RCPSP), typically solved via mixed-integer linear programming (MILP). However, unlike ideal models, real-world UAM operations are messy, and operator requests are frequently ambiguous. To handle this uncertainty, the proposed framework pairs a Bayesian network to infer intent via dialogue with Answer Set Programming (ASP) to categorize specific ambiguity types. Once the input is clarified, the system generates new MILP constraints and recalculates the schedule, allowing the operator to instantly verify changes against the initial plan.
Fields of Study & Subjects
Air Traffic Management and Optimization
AI-based Problem Solving and Planning
Scheduling and Optimization Algorithms
Publication details
- Journal / source
- Aerospace
- Volume
- 13
- Issue
- 2
- Pages
- 165–165
- Publication date
- 10 February 2026
- Language
- EN