Decision theory models for applications in artificial intelligence  concepts and solutions /  [electronic resource] : 
Other title :  InfoSci-Computer Science (selected 23 titles). 
L. Enrique Sucar, Eduardo F. Morales and Jesse Hoey, editors. 
 - electronic texts (384 p.) :  digital files. 
<br/><br/>Includes bibliographical references. 
<br/><br/>1. Introduction / L. Enrique Sucar, Eduardo Morales and Jesse Hoey -- 2. Introduction to Bayesian networks and influence diagrams / Luis Enrique Sucar -- 3. An introduction to fully and partially observable Markov decision processes / Pascal Poupart -- 4. An introduction to reinforcement learning / Eduardo F. Morales and Julio H. Zaragoza -- 5. Inference strategies for solving semi-Markov decision processes / Matthew Hoffman and Nando de Freitas -- 6. Multistage stochastic programming: a scenario tree based approach to planning under uncertainty / Boris Defourny, Damien Ernst and Louis Wehenkel -- 7. Automatically generated explanations for Markov decision processes / Omar Zia Khan, Pascal Poupart and James P. Black -- 8. Dynamic LIMIDS / Francisco J. Dz and Marcel A. J. van Gerven -- 9. Relational representations and traces for efficient reinforcement learning / Eduardo F. Morales and Julio H. Zaragoza -- 10. A decision-theoretic tutor for analogical problem solving / Kasia Muldner and Cristina Conati -- 11. Dynamic decision networks applications in active learning simulators / Julieta Noguez ... [et al.] -- 12. An intelligent assistant for power plant operation and training based on decision-theoretic planning / Alberto Reyes and Francisco Elizalde -- 13. POMDP models for assistive technology / Jesse Hoey, Pascal Poupart, Craig Boutilier and Alex Mihailidis -- 14. A case study of applying decision theory in the real world: POMDPs and spoken dialog systems / Jason D. Williams -- 15. Task coordination for service robots based on multiple Markov decision processes / Elva Corona and L. Enrique Sucar -- 16. Applications of DEC-MDPs in multi-robot systems / Aurie Beynier and Abdel-Illah Mouaddib. 
<br/><br/> Restricted to subscribers or individual electronic text purchasers. 
<br/><br/> 'This book provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence'-- 
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<br/><br/><br/>Mode of access: World Wide Web. 
<br/><br/><label>ISBN: </label>9781609601676 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial intelligence--Statistical methods.<br/>Bayesian statistical decision theory.
<br/><br/><label>Subjects--Index Terms: </label>Active learning simulators. Bayesian networks. Decision theoretic models. Health in the home. Dynamic decision networks applications. Markov decision processes. Intelligent assistants. Power plant operations. Multistage stochastic programming. Reinforcement learning. Semi-Markov decision processes. Task coordination for service robots.