• DocumentCode
    1087522
  • Title

    Improving Air Traffic Management with a Learning Multiagent System

  • Author

    Tumer, Kagan ; Agogino, Adrian

  • Author_Institution
    Oregon State Univ., Corvallis, OR
  • Volume
    24
  • Issue
    1
  • fYear
    2009
  • Firstpage
    18
  • Lastpage
    21
  • 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;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2009.10
  • Filename
    4763650
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1087522