Learning to Play
Springer International Publishing (Verlag)
978-3-030-59237-0 (ISBN)
After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography.
The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.
Prof. Aske Plaat is Professor of Data Science at Leiden University and scientific director of the Leiden Institute of Advanced Computer Science (LIACS). He is co-founder of the Leiden Centre of Data Science (LCDR) and initiated the SAILS stimulation program. His research interests include reinforcement learning, scalable combinatorial reasoning algorithms, games and self-learning systems.
Introduction.- Intelligence and Games.- Reinforcement Learning.- Heuristic Planning.- Adaptive Sampling.- Function Approximation.- Self-Play.- Conclusion.- App. A, Deep Reinforcement Learning Environments.- App. B, Running Python.- App. C, Tutorial for the Game of Go.- App. D, AlphaGo Technical Details.- References.- List of Figures.- List of Tables.- List of Algorithms.- Index.
| Erscheinungsdatum | 05.01.2021 |
|---|---|
| Zusatzinfo | XIII, 330 p. 111 illus., 72 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 682 g |
| Themenwelt | Informatik ► Software Entwicklung ► Spieleprogrammierung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Sozialwissenschaften ► Kommunikation / Medien ► Medienwissenschaft | |
| Schlagworte | Adaptive sampling • AlphaGo • Artificial Intelligence • Computational Intelligence • Deep learning • Evolutionary Computing • Games • Go • Heuristics • machine learning • Markov Decision Processes • Reinforcement Learning |
| ISBN-10 | 3-030-59237-5 / 3030592375 |
| ISBN-13 | 978-3-030-59237-0 / 9783030592370 |
| Zustand | Neuware |
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