Recent years have witnessed significant advances in technologies and services in modern network applications, including smart grid management, wireless communication, cybersecurity as well as multi-agent autonomous systems. Considering the heterogeneous nature of networked entities, emerging network applications call for game-theoretic models and learning-based approaches in order to create distributed network intelligence that responds to uncertainties and disruptions in a dynamic or an adversarial environment. This paper articulates the confluence of networks, games and learning, which establishes a theoretical underpinning for understanding multi-agent decision-making over networks. We provide an selective overview of game-theoretic learning algorithms within the framework of stochastic approximation theory, and associated applications in some representative contexts of modern network systems, such as the next generation wireless communication networks, the smart grid and distributed machine learning. In addition to existing research works on game-theoretic learning over networks, we highlight several new angles and research endeavors on learning in games that are related to recent developments in artificial intelligence. Some of the new angles extrapolate from our own research interests. The overall objective of the paper is to provide the reader a clear picture of the strengths and challenges of adopting game-theoretic learning methods within the context of network systems, and further to identify fruitful future research directions on both theoretical and applied studies.

翻译：近年来，现代网络应用在技术和服务的多个方面取得了显著进展，包括智能电网管理、无线通信、网络安全以及多智能体自主系统。考虑到网络实体异构的本质，新兴网络应用需要借助博弈论模型和基于学习的方法，以创建能够动态或对抗环境下应对不确定性和干扰的分布式网络智能。本文阐述了网络、博弈与学习的交汇，为理解网络上的多智能体决策奠定了理论基础。我们在随机逼近理论的框架下，选择性概述了博弈论学习算法及其在现代网络系统若干代表性场景中的应用，例如下一代无线通信网络、智能电网和分布式机器学习。除了现有的网络博弈学习研究外，我们重点探讨了与人工智能最新发展相关的博弈学习新视角和研究方向，其中部分新视角源于我们自身的研究兴趣。本文的总体目标是让读者清晰了解在网络系统中采用博弈论学习方法的优势与挑战，并进一步指明理论和应用研究中富有前景的未来研究方向。