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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems on Hardback by Tatiana Tatarenko
Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems on Hardback by Tatiana Tatarenko
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

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Description

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. 

Author Biography:

Tatiana Tatarenko received her Ph.D. from the Control Methods and Robotics Lab at the Technical University of Darmstadt, Germany in 2017. In 2011, she graduated with honors in Mathematics, focusing on statistics and stochastic processes, from Lomonosov Moscow State University, Russia. Her main research interests are in the fields of distributed optimization, game-theoretic learning, and stochastic processes in networked multi-agent systems. Currently, Dr. Tatarenko is a research assistant at TU Darmstadt, where she teaches and supervises students. 
Release date NZ
September 28th, 2017
Pages
171
Edition
1st ed. 2017
Audience
  • Professional & Vocational
Illustrations
38 Illustrations, black and white; IX, 171 p. 38 illus.
ISBN-13
9783319654782
Product ID
26871033

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