Description

LOCATION: Spine Auditorium B/W Bldg 4 & 5 Recently, there has been extensive research that employs distributed optimization, learning in games, and distributed control to model and analyze the performance of various networks; such as communication, biological and social networks. There are successful examples where distributed strategic learning and game theory provide a deeper understanding of network dynamics and lead to better design of efficient, scalable, stable and robust networks. Still, there remain many interesting open research problems to be identified and explored, and many issues to be addressed in learning in games. This talk will introduce the audience to some of the essential ingredients of learning in games, particularly mean field learning, cost-of-learning and combined learning and will discuss the current state of its applications in smart cities, cloud networking, big data and small cell networks.