Tatiana Tatarenko

Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems (eBook, PDF)

• Format: PDF

Produktbeschreibung

Presents new, efficient methods for optimization in large-scale multi-agent systems
Develops efficient optimization algorithms for three different information settings in multi-agent systems
Sets optimization problems without common restrictive assumptions

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 171
• Erscheinungstermin: 19. September 2017
• Englisch
• ISBN-13: 9783319654799
• Artikelnr.: 53058306

Autorenporträt

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.

Inhaltsangabe

Introduction and Research Motivation.- Backgrounds and Formulation of Contributions.- Logit Dynamics in Potential Games with Memoryless Players.- Stochastic Methods in Distributed Optimization and Game-Theoretic Learning.- Conclusion.- Appendix.

Rezensionen

"This book offers new efficient methods for optimization and control in multi-agent systems through the agency of game-theoretic learning. ... The book represents an important scientific contribution in the field of optimization for the multi-agent systems." (Vasile Postolica, zbMath 1415.91002, 2019)