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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems / by Tatiana Tatarenko.
Springer Nature - Springer Mathematics and Statistics eBooks 2017 English International Available online
View online- Format:
- Book
- Author/Creator:
- Tatarenko, Tatiana., Author.
- Language:
- English
- Subjects (All):
- System theory.
- Game theory.
- Computer science—Mathematics.
- Statistics.
- Mathematical optimization.
- Probabilities.
- Systems Theory, Control.
- Game Theory.
- Math Applications in Computer Science.
- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
- Optimization.
- Probability Theory and Stochastic Processes.
- Local Subjects:
- Systems Theory, Control.
- Game Theory.
- Math Applications in Computer Science.
- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
- Optimization.
- Probability Theory and Stochastic Processes.
- Physical Description:
- 1 online resource (IX, 171 p. 38 illus.)
- Edition:
- 1st ed. 2017.
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2017.
- Summary:
- 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. .
- Contents:
- 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.
- Notes:
- Includes bibliographical references.
- ISBN:
- 3-319-65479-9
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