Learning-Driven Game Theory for AI : Concepts, Models, and Applications

著者名：Salimi Ph.D., Mehdi (EDT)/Ahmadian PhD, Ali (EDT)

Morgan Kaufmann（2026/01/06発売）

• 言語：ENG
• ISBN：9780443438523
• eISBN：9780443438530

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

Table of Contents

Foundations and Applications of Game Theory1. Applications of Game Theory in Artificial Intelligence: A Review2. Applications of Game Theory in Climate Change Studies: A Review3. A Review on the Applications of Game Theory in Environmental Health4. Applications of game theory in renewable energy studies: A review5. Tourist-resident interactions in evolutionary games: tourism and sustainability6. Exploring the Evolution and Impact of Learning-Driven Game Theory for AI: A Bibliometric AnalysisGame Theory in Learning and AI Systems7. Evolutionary Game Dynamics of Learning in Neural Networks Through Replicator Equations8. Game-Based Ensemble Learning for Classifying Multi-Class Problems9. Fair Incentive Allocation in Vertical Federated Learning Using NucleolusGame Theory and Explainability in AI10. Several Perspectives on Explainable AI in Medicine: Game Theory Integrated Learning11. Inverse Game Theory for Preference Learning in Generative AI Systems: A Computational Complexity Framework12. MYerson Additive Explanations on Graphs (MYER): Advancement of Explainable Artificial Intelligence Using a Graphical ApproachMathematical Models and Adaptive Algorithms13. Integrating Game-Theoretic Learning with AI for Lung Cancer Diagnosis and Risk Prediction14. Truth as Geometry: A Topological Approach to Logic, Uncertainty, and AI Reasoning15. Pursuit–Evasion Differential Games with Gronwall-Type Constraints: A Theoretical Study16. Optimal pursuit time in a linear differential game with a Gronwall-type constraint17. Pursuit-Evasion Game under Lawden-Type Constraints18. Guaranteed Pursuit Time of a Linear Pursuit Differential Game with a Mixed Constraints on Players' Control Functions19. Adaptive Control of Opinion Dynamics on a Social Network with a Principal20. An Enhanced K-Means Clustering Approach: NBK-means Algorithm