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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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Informazioni sull?autore
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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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 scommunication 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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- EditoreSpringer
- Data di pubblicazione2018
- ISBN 10 331988039X
- ISBN 13 9783319880396
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine184
- Contatto del produttorenon disponibile
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Editore:
Springer International Publishing, 2018
ISBN 10: 331988039X
ISBN 13: 9783319880396
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Da: moluna, Greven, Germania
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Condizione: 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. Codice articolo 448761199
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Editore:
Springer International Publishing Aug 2018, 2018
ISBN 10: 331988039X
ISBN 13: 9783319880396
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783319880396
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
ISBN 10: 331988039X
ISBN 13: 9783319880396
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783319880396
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
ISBN 10: 331988039X
ISBN 13: 9783319880396
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783319880396
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. pp. IX, 171 38 illus. 1 Edition NO-PA16APR2015-KAP. Codice articolo 26384557503
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Condizione: New. Print on Demand pp. IX, 171 38 illus. Codice articolo 379346528
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Print on DemandDa: Biblios, Frankfurt am main, HESSE, Germania
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Condizione: New. PRINT ON DEMAND pp. IX, 171 38 illus. Codice articolo 18384557493
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Da: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. reprint edition. 171 pages. 9.25x6.10x0.42 inches. In Stock. Codice articolo 331988039X
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Paperback. Condizione: New. New. book. Codice articolo ERICA800331988039X6
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