Markov Decision Processes in Artificial Intelligence

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 agent wishes to decide which is its best strategy (do nothing, replace parts preventively, repair, change car, etc.) ... state (eg. we know the failure probability of an engine if the oil leak is not fixed), we can model this problem as an MDP.

Title:Markov Decision Processes in Artificial Intelligence
Author: Olivier Sigaud, Olivier Buffet
Publisher:John Wiley & Sons - 2013-03-04

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