AI-Powered UAM Scheduling: Intent-Driven Rescheduling for Urban Air Mobility
Research#AI Scheduling🔬 Research|Analyzed: Jan 26, 2026 11:43•
Published: Dec 17, 2025 14:04
•1 min read
•ArXivAnalysis
This research explores an innovative approach to managing the complex scheduling challenges of Urban Air Mobility (UAM), particularly within vertiports. By integrating three-valued logic for interpreting user intent with a decision tree, the study proposes a robust and explainable framework for dynamic UAM scheduling, optimizing resource allocation.
Key Takeaways
- •Addresses UAM scheduling complexities using a Mixed Integer Linear Programming (MILP) approach.
- •Employs three-valued logic and decision trees to understand and adapt to user rescheduling requests.
- •The system integrates Answer Set Programming (ASP) and MILP for optimized and explainable UAM scheduling.
Reference / Citation
View Original"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."