Team playing under uncertainties

Cooperation is an essential function in a wide array of network scenarios, including wireless, robotics, transports and beyond. In decentralized networks, cooperation (or team play) must be achieved by agents despite information uncertainties in the global state of the network. Cooperation in the presence of information uncertainties is a highly challenging problem for which no systematic optimization solution exist. In this talk, we describe different mechanisms for solving it, from information theoretic to machine learning based. In the machine learning approach this problem, we introduce so called Team Deep Learning Networks (Team-DNN) where agents learn to coordinate with each other under uncertainties. In the communication domain, we show how devices can learn how to message each other relevant information and take appropriate transmission decisions, possibly under the control of a meta-expert, so as to optimize network performance.