Learning-Driven Game Theory for AI: Concepts, Models, and Applications
Author(s): Mehdi Salimi Ph.D. (Editor), Ali Ahmadian PhD
- Publisher: Morgan Kaufmann
- Publication Date: January 29, 2026
- Edition: 1st
- Language: English
- Print length: 268 pages
- ISBN-10: 0443438528
- ISBN-13: 9780443438523
Book Description
Learning-Driven Game Theory for AI: Concepts, Models, and Applications offers in-depth coverage of recent methodological and conceptual advancements in various disciplines of Dynamic Games, namely differential and discrete-time dynamic games, evolutionary games, repeated and stochastic games, and their applications in a variety of fields, such as computer science, biology, economics, and management science. In this book, the authors bridge the gap between traditional game theory and its modern applications in artificial intelligence (AI) and related technological fields. The dynamic nature of contemporary problems in robotics, cybersecurity, machine learning, and multi-agent systems requires game-theoretic solutions that go beyond classical methods. The book delves into the rapidly growing intersection of pursuit differential games and AI, focusing on how these advanced game-theoretic models can be applied to modern AI systems, making it an indispensable resource for both academics and professionals. The book also provides a variety of applications demonstrating the practical integration of AI and game theory across various disciplines, such as autonomous systems, federated learning, and distributed decision-making frameworks. The book also explores the use of game theory in reinforcement learning, swarm intelligence, multi-agent coordination, and cybersecurity. These are critical areas where AI and dynamic games converge. Each chapter covers a different facet of dynamic games, offering readers a comprehensive yet focused exploration of topics such as differential and discrete-time games, evolutionary dynamics, and repeated and stochastic games. The absence of static games ensures a concentrated focus on the dynamic, evolving problems that are most relevant today.
- Offers comprehensive coverage of advanced games while focusing on cutting-edge AI applications
- Includes case studies that illustrate the application of game theory in AI-driven fields like reinforcement learning, swarm intelligence, and cybersecurity
- Provides readers with a practical focus, combined with the inclusion of emerging methodologies like learning-based approaches to pursuit-evasion games
- Equips readers with tools and frameworks to tackle the complex, dynamic challenges in their fields
Editorial Reviews
Review
The definitive guide for those looking to understand, develop, and apply dynamic game theory with AI to solve complex real-world problems
From the Back Cover
Learning-Driven Game Theory for AI: Concepts, Models, and Applications offers in-depth coverage of recent methodological and conceptual advancements in various disciplines of Dynamic Games, namely differential and discrete-time dynamic games, evolutionary games, repeated and stochastic games, and their applications in a variety of fields, such as computer science, biology, economics, and management science. In this book, the authors bridge the gap between traditional game theory and its modern applications in artificial intelligence (AI) and related technological fields. The dynamic nature of contemporary problems in robotics, cybersecurity, machine learning, and multi-agent systems requires game-theoretic solutions that go beyond classical methods. The book delves into the rapidly growing intersection of pursuit differential games and AI, focusing on how these advanced game-theoretic models can be applied to modern AI systems, making it an indispensable resource for both academics and professionals. The book also provides a variety of applications demonstrating the practical integration of AI and game theory across various disciplines, such as autonomous systems, federated learning, and distributed decision-making frameworks. The book also explores the use of game theory in reinforcement learning, swarm intelligence, multi-agent coordination, and cybersecurity. These are critical areas where AI and dynamic games converge. Each chapter covers a different facet of dynamic games, offering readers a comprehensive yet focused exploration of topics such as differential and discrete-time games, evolutionary dynamics, and repeated and stochastic games. The absence of static games ensures a concentrated focus on the dynamic, evolving problems that are most relevant today.
About the Author
Dr. Mehdi Salimi is an accomplished faculty member with a diverse academic background, having held positions at several Canadian universities, including KPU, StFX, and McMaster University. He earned his Ph.D. in applied mathematics from UPM in 2011 and previously obtained a master’s degree in pure mathematics from Tehran, Iran, in 2006. Dr. Salimi has served as a visiting professor at UniRC and a research fellow at the MEDAlics Research Centre at the University “Dante Alighieri” in Italy. He completed a postdoctoral fellowship at the Center for Dynamics (CfD) at Dresden University of Technology, Germany, in January 2015 and is now a senior member of the faculty there. Additionally, he is part of the Decisions Lab at DiGiES, University of Reggio Calabria, Italy. Dr. Salimi has published around 80 articles, showcasing his expertise in Game Theory, Dynamical Systems, and Data Science. His contributions extend to editorial roles in several prestigious journals, reflecting his commitment to advancing scientific knowledge and fostering academic dialogue in his fields of interest. Moreover, Dr. Salimi has made valuable contributions as an editorial member of esteemed journals, including Mathematical Methods of Operations Research, Journal of Optimization Theory and Applications, Annals of Operations Research, Soft Computing, Dynamic Games and Applications, Results in Control and Optimization, Engineering Science and Technology an International Journal, Mathematics, and Sustainable Computing: Informatics and Systems.
Dr. Ali Ahmadian is a senior research scientist the Mediterranea University of Reggio Calabria, Italy and an Associate Fellow Researcher at the Institute of IR 4.0, The National University of Malaysia. As an active researcher, he is dedicated to research in applied mathematics. He received his Ph.D. in early of 2014 as the best postgraduate student from Universiti Putra Malaysia (UPM). After his Ph.D. He took a postdoctoral fellowship at same university as part of the numerical analysis research group and at the same time joint to the University of Malaya as an Associate Researcher. He was promoted as a Fellow Researcher on December 2017 in UPM. In 2019, he joined the National University of Malaysia as a senior lecturer where he currently works in the Institute of IR 4.0. In general, his primary mathematical focus is the development of computational methods and models for problems arising in computer science, biology, physics, and engineering under fuzzy and fractional calculus (FC); in this context, he has worked on projects related to nano-communication networks, drug delivery systems, acid hydrolysis in palm oil frond, and carbon nanotubes dynamics, nanofluids, viscosity, AI and etc. He is a member of editorial board in Sustainable Computing: Informatics and Systems, Soft Computing, Journal of control and Decision, Progress in Fractional Differentiation and Applications (Natural Sciences Publishing) and Mathematical Problems in Engineering (Hindawi) and lead guest editor of special issues in more than 50 international journals from Elsevier, Springer, Wiley and so on. He is an author of more than 350 research papers published in as Nature, IEEE, Elsevier, Springer, Wiley and etc. He is an author and editor of four books published and in process in CRC Press and Elsevier, respectively. He also presented his research works in 38 international conferences held in Canada, Serbia, China, Turkey, Malaysia and UAE. He also received Premio Anassilaos Giovani Award for his achievement in art, culture, economics and science from Italy in November 2021.
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