Markov Decision Processes in Artificial Intelligence
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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 (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.…mehr

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Produktbeschreibung
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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Autorenporträt
Olivier Sigaud is a Professor of Computer Science at the University of Paris 6 (UPMC). He is the Head of the "Motion" Group in the Institute of Intelligent Systems and Robotics (ISIR). Olivier Buffet has been an INRIA researcher in the Autonomous Intelligent Machines (MAIA) team of theLORIA laboratory, since November 2007.