Markov Decision Processes in Artificial Intelligence

Markov Decision Processes in Artificial Intelligence

Editor(s): Olivier Sigaud, Olivier Buffet

Published Online: 7 MAR 2013 10:49PM EST

Print ISBN: 9781848211674

Online ISBN: 9781118557426

DOI: 10.1002/9781118557426

About this Book

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.

Table of contents

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  1. Part 1: MDPs - Models and Methods

  2. Part 2: Beyond MDPs

    1. Chapter 8

      Stochastic Games (pages 229–276)

      Andriy Burkov, Laëtitia Matignon and Brahim Chaib-Draa

    1. Chapter 9

      DEC-MDP/POMDP (pages 277–318)

      Aurélie Beynier, François Charpillet, Daniel Szer and Abdel-Illah Mouaddib

    1. Chapter 10

      Non-Standard Criteria (pages 319–360)

      Matthieu Boussard, Maroua Bouzid, Abdel-Illah Mouaddib, Régis Sabbadin and Paul Weng

  3. Part 3: Applications

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