Angielski
This book covers artificial intelligence methods applied to games, both in research and game development. It is aimed at graduate students, researchers, game developers, and readers with a technical background interested in the intersection of AI and games. The book covers a range of AI methods, from traditional search, planning, and optimization, to modern machine learning methods, including diffusion models and large language models. It discusses applications to playing games, generating content, and modeling players, including use cases such as level generation, game testing, intelligent non-player characters, player retention, player experience analysis, and game adaptation. It also covers the use of games, including video games, to test and benchmark AI algorithms. The book is informed by decades of research and practice in the field and combines insights into game design with deep technical knowledge from the authors, who have pioneered many of the methods and approaches used in the field.
This second edition of the 2018 textbook captures significant developments in AI and gaming over the past 7 years, incorporating advancements in computer vision, reinforcement learning, deep learning, and the emergence of transformer-based large language models and generative AI. The book has been reorganized to provide an updated overview of AI in games, with separate sections dedicated to AI's core uses in playing and generating games, and modeling their players, along with a new chapter on ethical considerations. Aimed at readers with foundational AI knowledge, the book primarily targets three audiences: graduate or advanced undergraduate students pursuing careers in game AI, AI researchers and educators seeking teaching resources, and game programmers interested in creative AI applications. The text is complemented by a website featuring exercises, lecture slides, and additional educational materials suitable for undergraduate and graduate courses.
