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

Tatarenko, Tatiana

ISBN 10: 3319654780 ISBN 13: 9783319654782

Editorial: Springer, 2017

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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.

Acerca del autor: 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.

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T�tulo: Game-Theoretic Learning and Distributed ...
Editorial: Springer
A�o de publicaci�n: 2017
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Condici�n: New

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

Tatiana Tatarenko

Publicado por Springer International Publishing, 2017

ISBN 10: 3319654780 ISBN 13: 9783319654782

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Gebunden. Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 Lo. N� de ref. del art�culo: 154279814

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

Tatiana Tatarenko

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

Tatarenko, Tatiana

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ISBN 10: 3319654780 ISBN 13: 9783319654782

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

Tatarenko, Tatiana

Publicado por Springer, 2017

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

Tatiana Tatarenko

Publicado por Springer International Publishing, Springer Nature Switzerland Sep 2017, 2017

ISBN 10: 3319654780 ISBN 13: 9783319654782

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Buch. Condici�n: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 184 pp. Englisch. N� de ref. del art�culo: 9783319654782

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

Tatiana Tatarenko

Publicado por Springer International Publishing, 2017

ISBN 10: 3319654780 ISBN 13: 9783319654782

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

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Buch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de ref. del art�culo: 9783319654782

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

Tatiana Tatarenko

Publicado por Springer International Publishing Sep 2017, 2017

ISBN 10: 3319654780 ISBN 13: 9783319654782

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Buch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 184 pp. Englisch. N� de ref. del art�culo: 9783319654782

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