Name: Learning-Driven Game Theory for AI. Concepts, Models, and Applications
Price: 218482.00 ARS
Availability: InStock
Author: Ali, PhD Ahmadian;Mehdi, Ph.D. Salimi
ISBN: 9780443438523

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

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

Formato

Libro Físico

Editorial

Morgan Kaufmann Publishers In

Autor

Ali, PhD Ahmadian;Mehdi, Ph.D. Salimi

Año

2026

N° páginas

268

Encuadernación

Tapa Blanda

Dimensiones

27.60 x 21.60 cm

ISBN13

9780443438523

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

Ali, Phd Ahmadian;Mehdi, Ph.d. Salimi (Autor) · Morgan Kaufmann Publishers In · Tapa Blanda

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Reseña del libro "Learning-Driven Game Theory for AI. Concepts, Models, and Applications"

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.