Markov Decision Processes in Artificial Intelligence / Edition 1

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Overview

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrative applications.

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Editorial Reviews

From the Publisher
"As an overall conclusion, this book is an extensive presentation of MDPs and their applications in modeling uncertain decision problems and in reinforcement learning." (Zentralblatt MATH, 2011)

"The range of subjects covered is fascinating, however, from game-theoretical applications to reinforcement learning, conservation of biodiversity and operations planning. Oriented towards advanced students and researchers in the fields of both artificial intelligence and the study of algorithms as well as discrete mathematics." (Book News, September 2010)

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Product Details

  • ISBN-13: 9781848211674
  • Publisher: Wiley
  • Publication date: 3/29/2010
  • Series: ISTE Series , #447
  • Edition number: 1
  • Pages: 480
  • Product dimensions: 6.40 (w) x 9.20 (h) x 1.30 (d)

Table of Contents

List of Authors

Pt. 1 MDPs: Models and Methods 1

Ch. 1 Markov Decision Processes Frederick Garcia Garcia, Frederick Emmanuel Rachelson Rachelson, Emmanuel 3

Ch. 2 Reinforcement Learning Olivier Sigaud Sigaud, Olivier Frederick Garcia Garcia, Frederick 39

Ch. 3 Approximate Dynamic Programming Remi Munos Munos, Remi 67

Ch. 4 Factored Markov Decision Processes Thomas Degris Degris, Thomas Olivier Sigaud Sigaud, Olivier 99

Ch. 5 Policy-Gradient Algorithms Olivier Buffet Buffet, Olivier 127

Ch. 6 Online Resolution Techniques Laurent Peret Peret, Laurent Frederick Garcia Garcia, Frederick 153

Pt. 2 Beyond MDPs 185

Ch. 7 Partially Observable Markov Decision Processes Alain Dutech Dutech, Alain Bruno Scherrer Scherrer, Bruno 187

Ch. 8 Stochastic Games Andriy Burkov Burkov, Andriy Laetitia Matignon Matignon, Laetitia Brahim Chaib-Draa Chaib-Draa, Brahim 229

Ch. 9 Dec-Mdp/Pomdp Aurelie Beynier Beynier, Aurelie Francois Charpillet Charpillet, Francois Daniel Szer Szer, Daniel Abdel-Illah Mouaddib Mouaddib, Abdel-Illah 277

Ch. 10 Non-Standard Criteria Matthieu Boussard Boussard, Matthieu Maroua Bouzid Bouzid, Maroua Abdel-Illah Mouaddib Mouaddib, Abdel-Illah Regis Sabbadin Sabbadin, Regis Paul Weng Weng, Paul 319

Pt. 3 Applications 361

Ch. 11 Online Learning for Micro-Object Manipulation Guillaume Laurent Laurent, Guillaume 363

Ch. 12 Conservation of Biodiversity Iadine Chades Chades, Iadine 375

Ch. 13 Autonomous Helicopter Searching for a Landing Area in an Uncertain Environment Patrick Fabiani Fabiani, Patrick Florent Teichteil-Konigsbuch Teichteil-Konigsbuch, Florent 395

Ch. 14 Resource Consumption Control for an Autonomous Robot Simon Le Gloannec Le Gloannec, Simon Abdel-Illah Mouaddib Mouaddib, Abdel-Illah 413

Ch. 15 Operations Planning Sylvie Thiebaux Thiebaux, Sylvie Olivier Buffet Buffet, Olivier 425

Index 453

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