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

Descripción

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 ref. del artículo 9783319880396

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Detalles bibliográficos

Título

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

Editor

Springer International Publishing Aug 2018

Fecha de publicación

2018

Idioma

Inglés

ISBN 10

331988039X

ISBN 13

9783319880396

Encuadernación

Taschenbuch

Estado

Neu

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