Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems (Hardcover, 1st ed. 2017)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	September 2017
First published:	2017
Authors:	Tatiana Tatarenko
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	171
Edition:	1st ed. 2017
ISBN-13:	978-3-319-65478-2
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Science & Mathematics > Mathematics > Optimization > Game theory Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Promotions
LSN:	3-319-65478-0
Barcode:	9783319654782

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