The Confluence of Networks, Games and Learning
- URL: http://arxiv.org/abs/2105.08158v2
- Date: Sat, 26 Aug 2023 16:29:18 GMT
- Title: The Confluence of Networks, Games and Learning
- Authors: Tao Li, Guanze Peng, Quanyan Zhu and Tamer Basar
- Abstract summary: Emerging network applications call for game-theoretic models and learning-based approaches to create distributed network intelligence.
This paper articulates the confluence of networks, games and learning, which establishes a theoretical underpinning for understanding multi-agent decision-making over networks.
- Score: 26.435697087036218
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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