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

Authors: Tatarenko, Tatiana

  • Presents new, efficient methods for optimization in large-scale multi-agent systems
  • Develops efficient optimization algorithms for three different information settings in multi-agent systems
  • Sets optimization problems without common restrictive assumptions
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eBook $74.99
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  • ISBN 978-3-319-65479-9
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Hardcover $99.00
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  • ISBN 978-3-319-65478-2
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About this book

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. 



About the authors

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. 

Table of contents (5 chapters)

  • Introduction

    Tatarenko, Tatiana

    Pages 1-5

  • Game Theory and Multi-Agent Optimization

    Tatarenko, Tatiana

    Pages 7-26

  • Logit Dynamics in Potential Games with Memoryless Players

    Tatarenko, Tatiana

    Pages 27-91

  • Stochastic Methods in Distributed Optimization and Game-Theoretic Learning

    Tatarenko, Tatiana

    Pages 93-155

  • Conclusion

    Tatarenko, Tatiana

    Pages 157-158

Buy this book

eBook $74.99
price for USA (gross)
  • ISBN 978-3-319-65479-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $99.00
price for USA
  • ISBN 978-3-319-65478-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Authors
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-65479-9
DOI
10.1007/978-3-319-65479-9
Hardcover ISBN
978-3-319-65478-2
Edition Number
1
Number of Pages
IX, 171
Number of Illustrations and Tables
38 b/w illustrations
Topics