Game-theoretic learning and distributed optimization in memoryless multi-agent systems

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. Front Matter ....Pages i-ix Introduction (Tatiana Tatarenko)....Pages 1-5 Game Theory and Multi-Agent Optimization (Tatiana Tatarenko)....Pages 7-26 Logit Dynamics in Potential Games with Memoryless Players (Tatiana Tatarenko)....Pages 27-91 Stochastic Methods in Distributed Optimization and Game-Theoretic Learning (Tatiana Tatarenko)....Pages 93-155 Conclusion (Tatiana Tatarenko)....Pages 157-158 Back Matter ....Pages 159-171