ISBN: 9783319880396 - game-theoretic learning and distributed optimization in memoryless multi-agent systems (11 risultati)

Condizione: New. pp. IX, 171 38 illus. Softcover reprint of the original 1st ed. 2017 edition NO-PA16APR2015-KAP.

Paperback. Condizione: Brand New. reprint edition. 171 pages. 9.25x6.10x0.42 inches. In Stock.

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.…

Taschenbuch. Condizione: 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. …

Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condizione: new. Questo è un articolo print on demand.

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.…

Condizione: New. Print on Demand pp. IX, 171 38 illus.

Condizione: New. PRINT ON DEMAND pp. IX, 171 38 illus.

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.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Presents new, efficient methods for optimization in large-scale multi-agent systemsDevelops efficient optimization algorithms for three different information settings in multi-agent systemsSets optimization problems without common restrictive assumptionsSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 184 pp. Englisch.…