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Catalogue Information
Field name	Details
Dewey Class	519
Title	Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems ([EBook]) / by Tatiana Tatarenko.
Author	Tatarenko, Tatiana
Other name(s)	SpringerLink (Online service)
Publication	Cham : : Springer International Publishing : : Imprint: Springer, , 2017.
Physical Details	IX, 171 p. 38 illus. : online resource.
ISBN	9783319654799
Summary Note	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.?.:
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site	http://dx.doi.org/10.1007/978-3-319-65479-9
Links to Related Works	Subject References: Computer Science . Game Theory . Math Applications in Computer Science . Mathematical optimization . Mathematics . Optimization . Probabilities . Probability theory and stochastic processes . Statistics . Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences . System theory . Systems Theory, Control . Authors: Tatarenko, Tatiana . Corporate Authors: SpringerLink (Online service) . Classification: 519 .

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

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