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Decision Making Under Uncertainty: Theory and Application (MIT Lincoln Laboratory Series) Illustrated Edición

de Mykel J. Kochenderfer (Author), Christopher Amato (Contributor), Girish Chowdhary (Contributor),
4.1 4.1 de 5 estrellas 25 calificaciones
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An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance.

Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance.

Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance.

Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Diapositiva anterior de detalles del producto
  1. ISBN-10
    0262029251
  2. ISBN-13
    978-0262029254
  3. Edición
    Illustrated
  4. Editorial
    The MIT Press
  5. Fecha de publicación
    17 Julio 2015
  6. Parte de la serie
    MIT Lincoln Laboratory Series
  7. Idioma
    Inglés
  8. Dimensiones
    9.1 x 7 x 1.1 pulgadas
  9. Número de páginas
    352 páginas
  10. Ver todos los detalles
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Conjunto anterior de diapositivas
  1. Algorithms for Decision Making
  2. Information Theory, Inference and Learning Algorithms
  3. Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)
  4. Algorithms for Optimization (Mit Press)
    Algorithms for Optimization (Mit Press)
    Mykel J. Kochenderfer
    4.7 de 5 estrellas 118
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    PVPR: US$95.00
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    US$9.66 de envío
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  5. Applied State Estimation and Association (MIT Lincoln Laboratory Series)
    Applied State Estimation and Association (MIT Lincoln Laboratory Series)
    Chaw-Bing Chang
    5.0 de 5 estrellas 2
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  6. Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)
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Opiniones editoriales

Biografía del autor

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Detalles del producto

  • Editorial ‏ : ‎ The MIT Press; Illustrated edición (17 Julio 2015)
  • Idioma ‏ : ‎ Inglés
  • Tapa dura ‏ : ‎ 352 páginas
  • ISBN-10 ‏ : ‎ 0262029251
  • ISBN-13 ‏ : ‎ 978-0262029254
  • Edad de lectura ‏ : ‎ A partir de 18 años
  • Curso ‏ : ‎ 12 and up
  • Dimensiones ‏ : ‎ 9.1 x 7 x 1.1 pulgadas
  • Opiniones de clientes:
    4.1 4.1 de 5 estrellas 25 calificaciones

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4.1 de 5 estrellas
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Found out there is a missing chapter in the print. Instead of original chapter 5 there is a irrelevant chapter of learning biblical Hebrew.
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Yegor
5.0 de 5 estrellas a unique book
Calificado en Estados Unidos el 17 de agosto de 2015
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During my time as a student at Stanford, I had the pleasure of taking a CS course that used this book.

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.
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Steve Elston
5.0 de 5 estrellas Good introduction to the subject
Calificado en Estados Unidos el 30 de agosto de 2018
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If you are trying to dig into probabilistic modeling and decision theory, this book is a good place to start. The level of mathematical detail is minimal and the discussion is quite clear.

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.
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Michael Roberson
1.0 de 5 estrellas More of half introduction / overview and half a collection of research papers
Calificado en Estados Unidos el 7 de enero de 2019
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Very disappointed - barely an undergraduate level text in both depth and scope. Limited and fragmented chapters that move from concept to concept without digging into implementation or connecting a broader theory. The applications are focused mostly to aeronautical systems, one chapter for video surveillance, one chapter for speech processing, and one completely non-technical end chapter about how humans and systems should interact. The fact that they are written by different authors further lends to the disjointed, aimless feel of the overall textbook. Rather than introduce interesting, varied applications within the first half as concepts are introduced, applications are presented in very specific instances separate from general development of the theory. Not a good way to connect the dots. Most useful for the bibliography which lists references that actually get inside the material. I would recommend this book to an undergraduate aerospace engineer with no background in applied probability or decision theory. For all else, it is much too paltry to serve as a long-term reference or survey of decision theory / computational statistics.
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Cannon Gray LLC
5.0 de 5 estrellas Good job!
Calificado en Estados Unidos el 9 de agosto de 2017
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This is a really good book. Very clear and concise look under the hood of advanced AI systems. It's a textbook, not an airplane book - though aircraft feature prominently - but the technical parts are very well-explained. I'm just an applied statistician and was able grasp most of it. Good job!
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purplerhino
5.0 de 5 estrellas The excellent presentation of complex material makes this book amazing for ...
Calificado en Estados Unidos el 4 de enero de 2016
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The excellent presentation of complex material makes this book amazing for what books were designed for: self study, and sets this book apart from many other texts in the field. In the first part of the text Kochenderfer guides the reader through the theory necessary to solve multiple applied examples, which he presents at the end of the book in a detailed manner.
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Mageek
5.0 de 5 estrellas An excellent MDP / POMDP resource
Calificado en Estados Unidos el 12 de agosto de 2015
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An excellent overview of decision making theory, covering the basics of probability and probability models, games, Markov decision processes, and partially observable Markov decision processes. The book is well formatted and uses a consistent, clear, and concise mathematical style.
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.
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Prof WuZhai
2.0 de 5 estrellas Not a good book to learn from
Calificado en Estados Unidos el 4 de diciembre de 2018
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This book reads like an expanded annotated bibliography. It works well as a reference but certainly not a book to learn from if you do not have the basics. There are many ideas/concepts that would seem 'vague' if you haven't encountered them before.
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Brian Haney
4.0 de 5 estrellas Good buy
Calificado en Estados Unidos el 6 de mayo de 2018
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Good book. Does a great job of explaining complicated concepts in detail with examples. It could definitely be improved by including a glossary of terms with definitions. The book is challenging because of the complexity of the information, but well organized and well written.
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