Game Theoretic Learning and Distributed Optimization in Memoryless Multi Agent S

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  • Engels
  • Hardcover
  • 9783319654782
  • 28 september 2017
  • 171 pagina's
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Samenvatting

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.

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.



Productspecificaties

Inhoud

Taal
en
Bindwijze
Hardcover
Oorspronkelijke releasedatum
28 september 2017
Aantal pagina's
171
Illustraties
Nee

Betrokkenen

Hoofdauteur
Tatiana Tatarenko

Overige kenmerken

Editie
1st ed. 2017
Extra groot lettertype
Nee
Product breedte
155 mm
Product lengte
235 mm
Studieboek
Nee
Verpakking breedte
155 mm
Verpakking hoogte
235 mm
Verpakking lengte
235 mm
Verpakkingsgewicht
4026 g

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EAN
9783319654782
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