Buch, Englisch, 210 Seiten, Paperback, Format (B × H): 187 mm x 235 mm
Reihe: Synthesis Lectures on Artificial Intelligence and Machine Learning
Buch, Englisch, 210 Seiten, Paperback, Format (B × H): 187 mm x 235 mm
Reihe: Synthesis Lectures on Artificial Intelligence and Machine Learning
ISBN: 978-1-60845-886-8
Verlag: MORGAN & CLAYPOOL
This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems.
Autoren/Hrsg.
Weitere Infos & Material
- Introduction
- MDPs
- Fundamental Algorithms
- Heuristic Search Algorithms
- Symbolic Algorithms
- Approximation Algorithms
- Advanced Notes