- Series: MIT Lincoln Laboratory Series
- Hardcover: 352 pages
- Publisher: The MIT Press; 1 edition (July 17, 2015)
- Language: English
- ISBN-10: 0262029251
- ISBN-13: 978-0262029254
- Product Dimensions: 7 x 1.1 x 9 inches
- Shipping Weight: 1.7 pounds (View shipping rates and policies)
- Average Customer Review: 12 customer reviews
- Amazon Best Sellers Rank: #107,825 in Books (See Top 100 in Books)
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Decision Making Under Uncertainty: Theory and Application (MIT Lincoln Laboratory Series) 1st Edition
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This book is a tour de force for its systematic treatment of the latest advances in decision making and planning under uncertainty. The detailed discussion on modeling issues and computational efficiency within real-world applications makes it invaluable for students and practitioners alike.―David Hsu, Professor of Computer Science, National University of Singapore
This book is a thorough and authoritative treatment of the mathematics of planning and reasoning under uncertainty. The real-life case studies that end the book help ground the theory with concrete examples that can serve as models for researchers developing new applications of these powerful ideas. It would make a terrific text for a semester-long course on the subject of algorithmic decision making.―Michael L. Littman, Professor of Computer Science, Brown University
An intuitive and accessible introduction to the exciting topic of decision making under uncertainty―very timely given the latest advances in robotics and autonomous systems. Problems are framed in the probabilistic inference formulation and provide a modern take on the classical reinforcement learning paradigm under partial observability, with natural links to real-world applications.―Sethu Vijayakumar FRSE, Professor of Robotics, University of Edinburgh
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This is hands down the best introductory text I have come across on quantitative and computational methods for decision making and autonomous planning, with applications ranging from autonomous vehicle control to business decision making.
One reason this book is great is that it covers an incredible breadth of topics - everything from the foundations (decision making formalism, probabilistic modeling, sequential decision making basics) to rather advanced theory (POMDPs, newest advances in reinforcement learning) - without sacrificing the rigor and the depth of coverage. At the same time, the material is presented in a very logical order, which ensures that the new knowledge gradually builds on top of the theoretical foundation. The language of the book is plain, precise, concise and very easy to understand - even to people without advanced math background.
The quality of the math notation is in itself fascinating - the author has gone to great length to ensure all the math is very easy to read and comprehend. Finally, each chapter of the book provides an extensive literature review with up-to-date sources.
My impression is that this book could work well both as an introduction to the decision making methods, and as a review of a particular subfield. I strongly recommend this text.
Kochenderfer covers a large variety of methods for tackling decision making problems. Algorithms are clearly outlined and are straightforward to implement on one's own.
The level of detail in this book is good for initial learning, but not sufficient if you actually need to implement a particular solution. However, if you want the full detail on some particular subject there are good lists of suggested readings at the end of each chapter.
One unique aspect of this book are the applications chapters toward the end. These chapters are written by several authors, with experience in the area.