  Predicting the outcomes of cyber-physical systems with multiple human
interactions is a challenging problem. This article reviews a game theoretical
approach to address this issue, where reinforcement learning is employed to
predict the time-extended interaction dynamics. We explain that the most
attractive feature of the method is proposing a computationally feasible
approach to simultaneously model multiple humans as decision makers, instead of
determining the decision dynamics of the intelligent agent of interest and
forcing the others to obey certain kinematic and dynamic constraints imposed by
the environment. We present two recent exploitations of the method to model 1)
unmanned aircraft integration into the National Airspace System and 2) highway
traffic. We conclude the article by providing ongoing and future work about
employing, improving and validating the method. We also provide related open
problems and research opportunities.
