Markov Decision Processes in Artificial Intelligence

Markov Decision Processes in Artificial Intelligence

by Olivier Sigaud
     
 

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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 … See more details below

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.

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)

Product Details

ISBN-13:
9781118620106
Publisher:
Wiley
Publication date:
03/04/2013
Series:
ISTE
Sold by:
Barnes & Noble
Format:
NOOK Book
File size:
8 MB

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