Learning and Control in Multi-Agent Games on Networks

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Game theory has recently become an indispensable tool enabling the analysis, modeling and design of traditional as well as emerging complex networks. It is a powerful mathematical framework used to analyze strategic interactions of cooperative or competing decision-makers (called players or agents), while taking into account their preferences and requirements. The past decade has witnessed an explosion of interest in issues that intersect game theory and networks. There are many network-based applications in the fields of communication networks, the smart electric grid, the Internet of Things, social networks, network security, mobile service markets or even epidemic control. While there has been substantial progress, there are major challenges in applying traditional game theory to the understanding and design of complex modern networks. For example, the agents' goals (and decisions) interfere and this means that there is need for coordination. However, centralized coordination is often impractical in large networks (e.g. ad-hoc or peer-to-peer networks); or it consumes excessive bandwidth and energy. The proposed research program focuses on the extension of Nash's game theory to multi-agent networks. Our goal is to design nearest-neighbor interaction rules that achieve a desirable collective configuration (e.g. Nash equilibrium), rely on locally available information, minimize superfluous communication and processing overhead, and are provably convergent in general classes of games, and on general symmetric/asymmetric networks. We will focus on the following themes: (i) learning about the other agents, (ii) learning about the game (reinforcement learning), (iii) achieving robustness, resilience and dealing with malicious behaviour of the agents, and (iv) hybrid learning. Long-term our program will generate a theoretical framework that contributes to a general unified theory of games on networks. By combining a system-theoretic approach with operator theory, graph theory and networks, we will generate novel methodologies and algorithms, advancing the state-of-the-art in game theory on networks. Some of the by-products of this research will be general rules to deal with asymmetric and delayed networked information in optimizing network performance. This is important in the complex and highly heterogeneous networks around us. Applications range from wireless networks and social networks, to demand-response management in smart-grids and the design of networks of autonomous agents. It will also lead to insights and innovative technologies in the design of new network protocols, and in understanding networks such as social networks. Five PhD students, five Master students and one post-doctoral fellow will be trained.