Journal of Artificial Intelligence Research
Abstraction in Model Based Partially Observable Reinforcement Learning Using Extended Sequence Trees
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Bayesian affordance-based agent model for wayfinding behaviors in evacuation problems
DHM'13 Proceedings of the 4th International conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: healthcare and safety of the environment and transport - Volume Part I
The steady-state control problem for markov decision processes
QEST'13 Proceedings of the 10th international conference on Quantitative Evaluation of Systems
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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.