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

Tatarenko, Tatiana

9783319654782: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

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ISBN 10 : 3319654780 ISBN 13 : 9783319654782

Editeur : Springer International Publishing AG, 2017

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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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� propos de l?auteur

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.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteurSpringer International Publishing AG
Date d'�dition2017
ISBN 10 3319654780
ISBN 13 9783319654782
ReliureReli�
Langueanglais
Num�ro d'�dition1
Nombre de pages171
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9783319880396: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

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ISBN 10 : 331988039X ISBN 13 : 9783319880396
Editeur : Springer, 2018
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

Tatiana Tatarenko

Edit� par Springer International Publishing, 2017

ISBN 10 : 3319654780 ISBN 13 : 9783319654782

Neuf Couverture rigide

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Gebunden. Etat : 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 r�f. du vendeur 154279814

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

Tatiana Tatarenko

Edit� par Springer International Publishing, 2017

ISBN 10 : 3319654780 ISBN 13 : 9783319654782

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Buch. Etat : 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 r�f. du vendeur 9783319654782

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

Tatiana Tatarenko

Edit� par Springer International Publishing Sep 2017, 2017

ISBN 10 : 3319654780 ISBN 13 : 9783319654782

Neuf Couverture rigide

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : 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 r�f. du vendeur 9783319654782

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

Tatarenko, Tatiana

Edit� par Springer, 2017

ISBN 10 : 3319654780 ISBN 13 : 9783319654782

Neuf Couverture rigide

Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni

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Etat : New. In. N� de r�f. du vendeur ria9783319654782_new

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

Tatiana Tatarenko

Edit� par Springer International Publishing, Springer Nature Switzerland Sep 2017, 2017

ISBN 10 : 3319654780 ISBN 13 : 9783319654782

Neuf Couverture rigide

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : 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 r�f. du vendeur 9783319654782

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