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Sinopsis
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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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.
De la contraportada
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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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Springer International Publishing Sep 2017, 2017
ISBN 10: 3319654780
ISBN 13: 9783319654782
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Buch. Condición: 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. Nº de ref. del artículo: 9783319654782
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Springer International Publishing, 2017
ISBN 10: 3319654780
ISBN 13: 9783319654782
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783319654782
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Publicado por
Springer International Publishing, 2017
ISBN 10: 3319654780
ISBN 13: 9783319654782
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Librería: moluna, Greven, Alemania
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Gebunden. Condición: 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. Nº de ref. del artículo: 154279814
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Condición: New. pp. 162. Nº de ref. del artículo: 26375439220
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand pp. 162. Nº de ref. del artículo: 370606251
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Springer International Publishing AG, CH, 2017
ISBN 10: 3319654780
ISBN 13: 9783319654782
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Librería: Rarewaves.com UK, London, Reino Unido
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Hardback. Condición: New. 1st ed. 2017. 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. Nº de ref. del artículo: LU-9783319654782
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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Condición: New. SUPER FAST SHIPPING. Nº de ref. del artículo: 9783319654782
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
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