ABSTRACT
This chapter presents a unified view of learning algorithms with different levels of information. It provides the understanding of learning in strategic decision making that will become more acute with the increased deployment of wireless devices and limited resources. The learning approach in games formalizes this idea, and examines how, which and what kind of equilibrium might arise as a consequence of a long-run non-equilibrium process of learning, adaptation, and/or imitation. The games under state uncertainty are sometimes called robust games, that is in the class of games with incomplete information and imperfect payoffs. Fixed-point theorems play important roles in establishing existence of equilibria. Players may have limited knowledge about the status of other players, except perhaps for a small subset of neighboring players. The chapter also presents an overview of the key concepts discussed in this book.
