A multiagent approach to managing air traffic flow

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Multiagent Learning: Basics, Challenges, and Prospects (2012, background): “…the integration of learning in these real-world domains and has shown promising results that justify a stronger focus by the community on complex systems; for example, see Richter, Aberdeen, and Yu (2006); Kalyanakrish-nan, Liu, and Stone (2006); Abbeel et al. (2007); Agogino and Tumer (2012).”
Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems (2012, methodology): “However, it has been applied with success in some applications (Sen, Sekaran, & Hale, 1994; Sen & Sekaran, 1998; Busoniu et al., 2006; Wang & de Silva, 2006; Tumer & Agogino, 2007, 2010).”
Evolutionary Dynamics of Multi-Agent Learning: A Survey (2015, background): “Therefore, multi-agent systems can be used to model many complex problems of today's society, such as urban and air traffic control (Agogino & Tumer, 2012), multi-robot coordination (Ahmadi & Stone, 2006; Claes, Hennes, Tuyls, & Meeussen, 2012), distributed sensing (Mihaylov, Tuyls, & Nowé, 2014),…”