Tembine Distributed Strategic Learning for Wireless Engineers

1. Auflage 2012
ISBN: 978-1-4398-7644-2
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 496 Seiten

Although valued for its ability to allow teams to collaborate and foster coalitional behaviors among the participants, game theory’s application to networking systems is not without challenges. Distributed Strategic Learning for Wireless Engineers illuminates the promise of learning in dynamic games as a tool for analyzing network evolution and underlines the potential pitfalls and difficulties likely to be encountered.
Establishing the link between several theories, this book demonstrates what is needed to learn strategic interaction in wireless networks under uncertainty, randomness, and time delays. It addresses questions such as:

- How much information is enough for effective distributed decision making?

- Is having more information always useful in terms of system performance?

- What are the individual learning performance bounds under outdated and imperfect measurement?

- What are the possible dynamics and outcomes if the players adopt different learning patterns?

- If convergence occurs, what is the convergence time of heterogeneous learning?

- What are the issues of hybrid learning?

- How can one develop fast and efficient learning schemes in scenarios where some players have more information than the others?

- What is the impact of risk-sensitivity in strategic learning systems?

- How can one construct learning schemes in a dynamic environment in which one of the players do not observe a numerical value of its own-payoffs but only a signal of it?

- How can one learn "unstable" equilibria and global optima in a fully distributed manner?

The book provides an explicit description of how players attempt to learn over time about the game and about the behavior of others. It focuses on finite and infinite systems, where the interplay among the individual adjustments undertaken by the different players generates different learning dynamics, heterogeneous learning, risk-sensitive learning, and hybrid dynamics.

Zielgruppe

Electrical engineers and practitioners in wireless communications, networks, advanced mathematics, game theory, computer science, and economics.

Autoren/Hrsg.

Tembine, Hamidou

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Drahtlostechnologie
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Mobilfunk- und Drahtlosnetzwerke & Anwendungen
• Mathematik | Informatik Mathematik Operations Research Spieltheorie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Foreword by Tamer Basar
Preface by Eitan Altman
Introduction to Learning in Games
Basic Elements of Games
Robust Games in Networks

Basic Robust Games
Basic Robust Cooperative Games

Distributed Strategic Learning

Distributed Strategic Learning in Wireless Networks

Strategy-Learning
Introduction

Strategy-Learning under Perfect Action-Monitoring

Fully Distributed Strategy-Learning

Stochastic Approximations
Discussions and Open Issues

Payoff-Learning and Dynamics
Introduction

Learning Equilibrium Payoffs

Payoff Dynamics

Routing Games with Parallel Links

Numerical Values of Payoffs Are Not Observed

Combined Learning
Introduction

Model and Notations

Pseudo-Trajectory

Hybrid and Combined Dynamics

Learning in Games with Continuous Action Spaces

CODIPAS for Stable Games with Continuous Action Spaces

CODIPAS-RL via Extremum Seeking

Designer and Users in an Hierarchical System

From Fictitious Play with Inertia to CODIPAS-RL
CODIPAS-RL with Random Number of Active Players

CODIPAS for Multi-Armed Bandit Problems

CODIPAS and Evolutionary Game Dynamics

Fastest Learning Algorithms

Learning under Delayed Measurement
Introduction

Learning Under Delayed Imperfect Payoffs

Reacting to the Interference

Learning in Constrained Games
Introduction

Constrained One-Shot Games

Quality of Experience

Relevance in Qoe and Qos Satisfaction

Satisfaction Levels as Benchmarks

Satisfactory Solution

Efficient Satisfactory Solution

Learning Satisfactory Solution

From Nash Equilibrium to Satisfactory Solution

Mixed and Near-Satisfactory Solution

CODIPAS with Dynamic Satisfaction Level

Random Matrix Games
Mean-Variance Response and Demand Satisfaction

Learning under Random Updates
Introduction

Description of the Random Update Model

Fully Distributed Learning

Dynamic Routing Games with Random Traffic

Extensions

Mobility-Based Learning in Cognitive Radio Networks

Hybrid Strategic Learning

Quiz

Chapter Review

Fully Distributed Learning for Global Optima
Introduction

Resource Selection Games

Frequency Selection Games

User-Centric Network Selection

Markov Chain Adjustment

Pareto Optimal Solutions

Learning In Risk-Sensitive Games

Introduction

Risk-Sensitive in Dynamic Environment

Risk-Sensitive CODIPAS-RL

Risk-Sensitivity in Networking and Communications

Risk-Sensitive Mean Field Learning

Extensions
Chapter Review

A Appendix

A.1 Basics on Dynamical Systems

A.2 Basics on Stochastic Approximations

A.3 Differential Inclusion

A.4 Markovian Noise

Bibliography
Index