Game Theory and Deep Learning
Description
The book teaches readers how to develop a solid foundation in game theory and understand interactive scenarios in both engineering and everyday life, effectively apply their knowledge to practical problems, including resource allocation, security, and influence maximization, and finally, design strategies that optimally exploit available information through successive optimization, reinforcement learning, deep learning, and generative AI techniques.
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Persons
Dr. Lina Bariah received the M.Sc. and Ph.D. degrees in communications engineering from Khalifa University, Abu Dhabi, UAE, in 2015 and 2018, respectively. She was a Visiting Researcher with the Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada, in 2019, and an affiliate research fellow, James Watt School of Engineering, University of Glasgow, UK. She was a Senior Researcher at the technology Innovation institute, and a Lead AI Scientist at Open Innovation AI. She is currently an Adjunct Professor at Khalifa University, and an Adjunct Research Professor, Western University, Canada. Dr. Bariah serves as the Industry Chair for GenAINet ETI. Dr. Bariah is a senior member of the IEEE, IEEE Communications Society, IEEE Vehicular Technology Society, and IEEE Women in Engineering. She is the founder and lead of Women in Machine Learning and Data Science (WiMLDS)-Abu Dhabi Chapter. She was recently listed among the100 Brilliant and Inspiring Women in 6G", by Women in 6G organization. She has authored/co-authored 75+ research papers/book chapters in highly ranked journals and flagship conferences. She is an Editor at IEEE Transactions on Wireless Communications. She was an Associate Editor for the IEEE Communication Letters, an Associate Editor for the IEEE Open Journal of the Communications Society, and an Area Editor for Physical Communication (Elsevier). She is a Guest Editor in IEEE Communication Magazine, IEEE Network Magazine, and IEEE Open Journal of Vehicular Technology.
Content
2. Overview of deep learning
3. Overview of game Theory
4. Conventional deep learning and game theory (GANs, VAE, DMs, CNN, RNN,…).
5. Federated learning and game theory
6. Reinforcement learning and game theory
7. Mean field games and deep learning
8. Large Language Models and game theory
9. Wireless resource allocation (6G applications)
10. Smart grid applications (power consumption scheduling, load forecast, real-time pricing,…)
11. Agent-Based LLMs and game-theoretic paradigms for security