Title :
Improving Air Traffic Management with a Learning Multiagent System
Author :
Tumer, Kagan ; Agogino, Adrian
Author_Institution :
Oregon State Univ., Corvallis, OR
Abstract :
A fundamental challenge facing the aerospace industry is efficient, safe, and reliable air traffic management (ATM). On a typical day, more than 40,000 commercial flights operate in US airspace, and the number of flights is increasing rapidly. This paper shows how learning multiagent system helps improve ATM.
Keywords :
aerospace computing; air traffic; multi-agent systems; US airspace; aerospace industry; air traffic management; learning multiagent system; traffic reliability; Aerodynamics; Air safety; Air traffic control; Aircraft; Airports; Control systems; Delay; Guidelines; Multiagent systems; Weather forecasting; air traffic management; multiagent coordination; multiagent learning;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2009.10
