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Improving Air Traffic Management with a Learning Multiagent System

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2 Author(s)
Kagan Tumer ; Oregon State Univ., Corvallis, OR ; Adrian Agogino

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.

Published in:

IEEE Intelligent Systems  (Volume:24 ,  Issue: 1 )