A collection of original papers, first published in French, this volume is a self-contained introduction to the mathematical models of decision-making processes under uncertain conditions. MDPs provide a framework to simulate such activities within artificial intelligence paradigm. Not for the faint of heart, even the introductory chapter presupposes familiarity with sophisticated mathematical modeling techniques. 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. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)
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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