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- Дата: 15-01-2026, 02:54
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Автор: Mehdi Salimi, Ali Ahmadian
Издательство: Morgan Kaufmann/Elsevier
Год: 2026
Страниц: 270
Язык: английский
Формат: pdf (true)
Размер: 15.5 MB
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.
