商品描述
This book explores the integration of Mean Field Game (MFG) theory with machine learning (ML), presenting both theoretical foundations and practical applications. Drawing from extensive research, it provides insights into how MFG can improve various ML techniques, including supervised learning, reinforcement learning, and federated learning. MFG theory and ML are converging to address critical challenges in high-dimensional spaces and multi-agent systems. While ML has transformed industries by leveraging vast data and computational power, scalability and robustness remain key concerns. MFG theory, which models large populations of interacting agents, offers a mathematical framework to simplify and optimize complex systems, enhancing ML's efficiency and applicability. By bridging these two fields, this book aims to drive innovation in scalable and robust machine learning. The integration of MFG with ML not only expands research possibilities but also paves the way for more adaptive and intelligent systems. Through this work, the authors hope to inspire further exploration and development in this promising interdisciplinary domain. With case studies and real-world examples, this book serves as a guide for researchers and students in communications and networks seeking to harness MFG's potential in advancing ML. Industry managers, practitioners and government research workers in the fields of communications and networks will find this book a valuable resource as well.
商品描述(中文翻譯)
本書探討了均場博弈（Mean Field Game, MFG）理論與機器學習（Machine Learning, ML）的整合，呈現了理論基礎和實際應用。根據廣泛的研究，本書提供了MFG如何改善各種ML技術的見解，包括監督學習、強化學習和聯邦學習。
MFG理論和ML正在融合，以應對高維空間和多代理系統中的關鍵挑戰。儘管ML通過利用大量數據和計算能力改變了各行各業，但可擴展性和穩健性仍然是主要關注點。MFG理論通過建模大量互動代理，提供了一個數學框架，以簡化和優化複雜系統，從而提高ML的效率和適用性。
通過橋接這兩個領域，本書旨在推動可擴展和穩健的機器學習的創新。MFG與ML的整合不僅擴展了研究的可能性，還為更具適應性和智能的系統鋪平了道路。通過這項工作，作者希望激發在這個充滿潛力的跨學科領域中進一步探索和發展的熱情。透過案例研究和實際範例，本書為尋求利用MFG潛力推進ML的通信和網絡領域的研究人員和學生提供了指導。通信和網絡領域的行業經理、從業者及政府研究工作者也會發現本書是一個寶貴的資源。
作者簡介
Dr, Yuhan Kang received the B.S. degree from the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China, in 2019, and the Ph.D. degree with the Electrical and Computer Engineering Department, the University of Houston, Houston, TX, USA in 2023. Currently he is AI Research Scientist for Weichai America Corp. His research interest include mean-field game theory, machine learning, deep learning, Internet-of-Things networks, and optimization theory. Dr. Hao Gao received the B.E. degree in electrical and information engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2018, and Ph.D. degree in electrical engineering with the University of Houston, Houston, TX, USA in 2022. Currently he is senior Engineering in Samsung, California, USA. His research interests include mean field game, machine learning, and related applications in wireless communication. Dr. Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. Currently, he is a John and Rebecca Moores Professor in the Electrical and Computer Engineering Department as well as in the Computer Science Department at the University of Houston, Texas. Dr. Han received IEEE fellow since 2014, AAAS fellow since 2019, and ACM Fellow since 2024. Dr. Han is a 1% highly cited researcher since 2017 according to Web of Science. Dr. Han is also the winner of the 2021 IEEE Kiyo Tomiyasu Award (an IEEE Field Award), for outstanding early to mid-career contributions to technologies holding the promise of innovative applications, with the following citation: for contributions to game theory and distributed management of autonomous communication networks.
作者簡介(中文翻譯)
鄺宇涵博士於2019年獲得中國成都電子科技大學資訊與通信工程學院的學士學位，並於2023年在美國德克薩斯州休士頓的休士頓大學電機與計算機工程系獲得博士學位。目前，他是威海美國公司的人工智慧研究科學家。他的研究興趣包括均場博弈理論、機器學習、深度學習、物聯網網絡及優化理論。
高浩博士於2018年獲得中國武漢華中科技大學電氣與信息工程的工程學士學位，並於2022年在美國德克薩斯州休士頓的休士頓大學獲得電機工程的博士學位。目前，他是美國加州三星公司的高級工程師。他的研究興趣包括均場博弈、機器學習及其在無線通信中的相關應用。
韓竹博士於1997年獲得清華大學電子工程的學士學位，並於1999年和2003年分別在馬里蘭大學帕克分校獲得電機與計算機工程的碩士及博士學位。目前，他是德克薩斯州休士頓大學電機與計算機工程系及計算機科學系的約翰與瑞貝卡·穆爾斯教授。韓博士自2014年起獲得IEEE Fellow，2019年獲得AAAS Fellow，2024年獲得ACM Fellow。根據Web of Science，韓博士自2017年以來是1%高被引研究者。韓博士也是2021年IEEE Kiyo Tomiyasu Award（IEEE領域獎）的獲得者，因其在技術創新應用方面的早期至中期卓越貢獻，具體表彰其在博弈理論及自主通信網絡的分散管理方面的貢獻。
