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Full Description
Complex cognitive systems, such as social networks, robotic swarms, or biological networks, are composed of individual entities (the agents) whose actions typically arise from some sophisticated form of "social" interaction with other agents. For example, consider the way humans form their individual opinions about a certain phenomenon. The opinions take shape via repeated interactions with other individuals, whether through physical contact or virtually. A diffusion mechanism emerges through which opinions, information, or even fake news propagate.
Social learning also arises over man-made systems in the form of decision-making strategies by multiple agents interacting over a network. Consider a robotic swarm deployed over a hazardous area, where some robots operating under disadvantageous conditions (e.g., with limited visibility or partial information) would only be able to perform their task (such as saving a life during a rescue operation) by leveraging significant cooperation from other robots that have better access to critical information. Nature itself provides many other excellent examples of cooperative learning in the form of biological networks.
The main topic of this book relates to mechanisms for information diffusion and decision-making over graphs, and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment.
Contents
Dedication
Preface
Chapter 1. Introduction
Chapter 2. Bayesian Learning
Chapter 3. From Single-Agent to Social Learning
Chapter 4. Network Models
Chapter 5. Social Learning with Geometric Averaging
Chapter 6. Error Probability Performance
Chapter 7. Social Learning with Arithmetic Averaging
Chapter 8. Adaptive Social Learning
Chapter 9. Learning Accuracy under ASL
Chapter 10. Adaptation under ASL
Chapter 11. Partial Information Sharing
Chapter 12. Social Machine Learning
Chapter 13. Extensions and Conclusions
Appendices
References
About the Authors
