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Game-theoretic learning and distribu...

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Game-theoretic learning and distributed optimization in memoryless multi-agent systems

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Game-theoretic learning and distributed optimization in memoryless multi-agent systems/ by Tatiana Tatarenko.
作者:	Tatarenko, Tatiana.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	ix, 171 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-65479-9
ISBN:	9783319654799

Game-theoretic learning and distributed optimization in memoryless multi-agent systems
Tatarenko, Tatiana.

Game-theoretic learning and distributed optimization in memoryless multi-agent systems[electronic resource] /by Tatiana Tatarenko. - Cham :Springer International Publishing :2017. - ix, 171 p. :ill., digital ;24 cm.

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.

ISBN: 9783319654799

Standard No.: 10.1007/978-3-319-65479-9doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .T38 2017

Dewey Class. No.: 006.31

Game-theoretic learning and distributed optimization in memoryless multi-agent systems
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520 $a 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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