CIF: Small: Communication-Aware Decentralized Game-Theoretic Learning Algorithms for Networked Systems with Uncertainty
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At the core of the opportunities for many emerging technological networked systems, such as Internet-of-things, smart grid, transportation systems, lies decentralization which means the deployment of interlinked agents, e.g., sensors, appliances, and devices, that operate in harmony. An implicit assumption in the decentralized operations in these systems is that these agents agree or are able to resolve their differences in what is their objective. In large scale networked systems, barring unreasonable accuracy of environmental information and unjustifiable levels of coordination, agents cannot be sure of what other agents are optimizing. This project will enable practical and scalable decentralized solutions to signal processing and communication problems in technological networked systems where wireless network technologies provide the backbone for information exchange. Furthermore, this project will involve mentoring of PhD students, providing research experience to undergraduate students, and the development of a graduate-level course on learning and networks at Texas A&M University. The premise of this project is that when agents disagree on their objectives in a networked multi-agent system due to environmental uncertainty, they are playing a game against uncertainty, and the optimality criterion is then defined by game-theoretic notions. Upon adopting, game-theoretic equilibria as the optimality criterion, the project is divided into three major thrusts. The first thrust will develop decentralized algorithms based on game-theoretic notions, and will characterize finite-time outcomes when information aggregation or consensus in large-scale networked systems may not be possible. The second thrust aims to understand fundamental trade-offs in communication vs. optimality, and to develop decentralized algorithms that account for costly and faulty communication. The third thrust will seek to characterize the likelihood of converging to undesirable equilibria, and will use influence maximization methods to avoid bad outcomes. Overall, the project draws upon methods common in decentralized optimization, e.g., consensus, and in signal processing, e.g., communication censoring, and combines them with game-theoretic, best-response type, learning algorithms to design scalable and communication-aware decentralized algorithms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.