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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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eBook
Fachbuch
2017

Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

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ISBN
EAN
978-3-319-65479-9
9783319654799
Artikel-Nr.
KN6NDGD
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-21.8
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CHF 106.50
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83.34
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1
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This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space. 



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Stichwörter
Inhaltsverzeichnis
Introduction and Research Motivation.- Backgrounds and Formulation of Contributions.- Logit Dynamics in Potential Games with Memoryless Players.- Stochastic Methods in Distributed Optimization and Game-Theoretic Learning.- Conclusion.- Appendix.
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Autor/-in
Biografie
Tatiana Tatarenko received her Ph.D. from the Control Methods and Robotics Lab at the Technical University of Darmstadt, Germany in 2017. In 2011, she graduated with honors in Mathematics, focusing on statistics and stochastic processes, from Lomonosov Moscow State University, Russia. Her main research interests are in the fields of distributed optimization, game-theoretic learning, and stochastic processes in networked multi-agent systems. Currently, Dr. Tatarenko is a research assistant at TU Darmstadt, where she teaches and supervises students. 

Publikation
Schweiz
19.09.2017
Springer Verlag
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Sprache
Englisch
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eBook
PDF
171 Seiten
Leserkritik
“This book offers new efficient methods for optimization and control in multi-agent systems through the agency of game-theoretic learning. … The book represents an important scientific contribution in the field of optimization for the multi-agent systems.” (Vasile Postolică, zbMath 1415.91002, 2019)
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