/ game theory and artificial intelligence / game theory and deep learning / Künstliche Intelligenz / Mean-field games

This book is an exploration of differential and algorithmic intelligent game theory, offering a comprehensive look at its methods and applications across diverse fields. As the first installment in a pioneering series, it underscores the expanding influence of game theory in the physical, biological, social, and computational sciences, as well as engineering and technology.

 

Readers will delve into dynamic strategic interactions, exploring continuous, discrete, and quantum strategies. The volume addresses computational challenges in finding equilibria and presents efficient algorithms for computing Nash equilibria. It also highlights the role of game theory in artificial intelligence, examining its use in AI-assisted behavioral and evolutionary game theory, multi-agent systems, and adversary training in generative adversarial networks.

 

This book is essential for researchers, graduate students, and professionals in mathematics, computer science, and economics who are interested in applying game theory to analyze and design strategic systems. Offering a unique interdisciplinary perspective, combining differential game theory's continuous decision-making with algorithmic methodologies, this volume sets the stage for future installments, providing real-world case studies and applications that illustrate the practical use of game theory in dynamic, complex environments