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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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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Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Paperback. Zustand: Brand New. reprint edition. 171 pages. 9.25x6.10x0.42 inches. In Stock. Artikel-Nr. 331988039X
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: 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. Artikel-Nr. 9783319880396
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Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems | Tatiana Tatarenko | Taschenbuch | ix | Englisch | 2018 | Springer | EAN 9783319880396 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 115379252
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