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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. Preface - p. xiii 8. Chapter 1: Applications of game theory in artificial intelligence: a review - p. 1 9. Chapter 2: Applications of game theory in climate change studies: a review - p. 9 10. Chapter 3: A review on the applications of game theory in environmental health - p. 15 11. Chapter 4: Applications of game theory in renewable energy studies: a review - p. 23 12. Chapter 5: Tourist–resident interactions in evolutionary games: tourism and sustainability - p. 31 13. Chapter 6: Exploring the evolution and impact of learning-driven game theory for AI: a bibliometric analysis - p. 39 14. Chapter 7: Evolutionary game dynamics of learning in neural networks through replicator equations - p. 49 15. Chapter 8: Game-based ensemble learning for classifying multi-class problems - p. 63 16. Chapter 9: Fair incentive allocation in vertical federated learning using nucleolus - p. 79 17. Chapter 10: Several perspectives on explainable AI in medicine: game theory integrated learning - p. 95 18. Chapter 11: Inverse game theory for preference learning in generative AI systems: a computational complexity framework - p. 113 19. Chapter 12: Myerson additive explanations on graphs (MYER) - p. 119 20. Chapter 13: Integrating game-theoretic learning with AI for lung cancer diagnosis and risk prediction - p. 129 21. Chapter 14: Truth as geometry: a topological approach to logic, uncertainty, and AI reasoning - p. 141 22. Chapter 15: Pursuit–evasion differential games with Gronwall-type constraints: a theoretical study - p. 179 23. Chapter 16: Optimal pursuit time in a linear differential game with a Gronwall-type constraint - p. 187 24. Chapter 17: Pursuit–evasion game under Lawden-type constraints - p. 199 25. Chapter 18: Guaranteed pursuit time of a linear game with mixed constraints - p. 215 26. Chapter 19: Adaptive control of opinion dynamics on a social network with a principal - p. 223 27. Chapter 20: An enhanced K-means clustering approach: NEK-means algorithm - p. 239 28. Index - p. 249
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. Preface - p. xiii 8. Chapter 1: Applications of game theory in artificial intelligence: a review - p. 1 9. Chapter 2: Applications of game theory in climate change studies: a review - p. 9 10. Chapter 3: A review on the applications of game theory in environmental health - p. 15 11. Chapter 4: Applications of game theory in renewable energy studies: a review - p. 23 12. Chapter 5: Tourist–resident interactions in evolutionary games: tourism and sustainability - p. 31 13. Chapter 6: Exploring the evolution and impact of learning-driven game theory for AI: a bibliometric analysis - p. 39 14. Chapter 7: Evolutionary game dynamics of learning in neural networks through replicator equations - p. 49 15. Chapter 8: Game-based ensemble learning for classifying multi-class problems - p. 63 16. Chapter 9: Fair incentive allocation in vertical federated learning using nucleolus - p. 79 17. Chapter 10: Several perspectives on explainable AI in medicine: game theory integrated learning - p. 95 18. Chapter 11: Inverse game theory for preference learning in generative AI systems: a computational complexity framework - p. 113 19. Chapter 12: Myerson additive explanations on graphs (MYER) - p. 119 20. Chapter 13: Integrating game-theoretic learning with AI for lung cancer diagnosis and risk prediction - p. 129 21. Chapter 14: Truth as geometry: a topological approach to logic, uncertainty, and AI reasoning - p. 141 22. Chapter 15: Pursuit–evasion differential games with Gronwall-type constraints: a theoretical study - p. 179 23. Chapter 16: Optimal pursuit time in a linear differential game with a Gronwall-type constraint - p. 187 24. Chapter 17: Pursuit–evasion game under Lawden-type constraints - p. 199 25. Chapter 18: Guaranteed pursuit time of a linear game with mixed constraints - p. 215 26. Chapter 19: Adaptive control of opinion dynamics on a social network with a principal - p. 223 27. Chapter 20: An enhanced K-means clustering approach: NEK-means algorithm - p. 239 28. Index - p. 249
