Bridge the gap between theoretical strategy and its real-world impact on AI systems. This book offers in-depth, yet accessible discussions of essential ideas, complemented by a practical coding component in Jupyter Notebooks.

Organized in a fun, reference-style format, each chapter offers a stand-alone exploration of advanced topics like Generative Adversarial Networks, Bayesian belief formation, dynamic learning in games, evolutionary game theory, and cooperative game dynamics, as well as core fundamentals such as Nash equilibrium and minimax strategies. The book's hands-on approach lets you experiment directly with Python-based models, ensuring that theoretical insights translate into concrete, problem-solving techniques.

Ideal for AI practitioners seeking to incorporate game theory into their work, this book is also an invaluable resource for students of computer science, economics, and philosophy. Game theorists aiming to expand into artificial intelligence will likewise find it indispensable. By blending an approachable style with rigorous content, Artificial Intelligence and Game Theory helps readers elevate their AI systems through strategic game-theoretic insights.

You will:

Delve into Nash Equilibrium, evolutionary game theory, adversarial dynamics, cooperative strategies, and more. Apply game-theoretic models to real-world scenarios--autonomous vehicles, optimization tasks, multi-agent systems, and beyond. Experiment with Jupyter Notebooks for Python-based implementations of game-theoretic strategies. Elevate algorithmic decision-making in dynamic, competitive environments by integrating game theory into AI workflows.

Artificial Intelligence and Game Theory: Insights into Decision-Making Algorithms bridges the gap between theoretical strategy and its real-world impact on AI systems. Organized in a fun, reference-style format, each section offers a stand-alone exploration of advanced topics like Generative Adversarial Networks, Bayesian belief formation, dynamic learning in games, evolutionary game theory, and cooperative game dynamics, as well as core fundamentals such as Nash equilibrium and minimax strategies.

Readers will find in-depth yet accessible discussions of essential ideas, complemented by a practical coding component in Jupyter Notebooks. This hands-on approach lets you experiment directly with Python-based models, guaranteeing that theoretical insights translate into concrete, problem-solving techniques.

Ideal for AI practitioners seeking to incorporate game theory into their work, this book is also an invaluable resource for students of computer science, economics, and philosophy. Game theorists aiming to expand into artificial intelligence will likewise find it indispensable. By blending an approachable style with rigorous content, Artificial Intelligence and Game Theory helps readers elevate their AI systems through strategic game-theoretic insights.

What you will learn:

• Key Game Theory Concepts: Delve into Nash Equilibrium, evolutionary game theory, adversarial dynamics, cooperative strategies, and more.
• Practical AI Applications: Apply game-theoretic models to real-world scenarios--autonomous vehicles, optimization tasks, multi-agent systems, and beyond.
• Hands-On Coding: Experiment with Jupyter Notebooks for Python-based implementations of game-theoretic strategies.
• Strategic AI Development: Elevate algorithmic decision-making in dynamic, competitive environments by integrating game theory into AI workflows.

Who This Book Is For

Data scientists, and machine learning/deep learning engineers who seek to integrate game theory into their work to enhance decision-making processes. It also targets AI practitioners and developers looking to optimize decision-making algorithms through game theory principles.