  In this article, we introduce a game-theoretic learning framework for the
multi-agent wireless network. By combining learning in artificial intelligence
(AI) with game theory, several promising properties emerge such as obtaining
high payoff in the unknown and dynamic environment, coordinating the actions of
agents and making the adversarial decisions with the existence of malicious
users. Unfortunately, there is no free lunch. To begin with, we discuss the
connections between learning in AI and game theory mainly in three levels,
i.e., pattern recognition, prediction and decision making. Then, we discuss the
challenges and requirements of the combination for the intelligent wireless
network, such as constrained capabilities of agents, incomplete information
obtained from the environment and the distributed, dynamically scalable and
heterogeneous characteristics of wireless network. To cope with these, we
propose a game-theoretic learning framework for the wireless network, including
the internal coordination (resource optimization) and external adversarial
decision-making (anti-jamming). Based on the framework, we introduce several
attractive game-theoretic learning methods combining with the typical
applications that we have proposed. What's more, we developed a real-life
testbed for the multi-agent anti-jamming problem based on the game-theoretic
learning framework. The experiment results verify the effectiveness of the
proposed game-theoretic learning method.
