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Decision theory models for applications in artificial intelligence concepts and solutions /

"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"--

Bibliographic Details
Corporate Author: IGI Global.
Other Authors: Sucar, L. Enrique, 1957-, Morales, Eduardo F., Hoey, Jesse.
Format: Electronic
Language:English
Published: Hershey, Pa. : IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA), 2011.
Subjects:
Artificial intelligence > Statistical methods.
Bayesian statistical decision theory.
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
Online Access:Chapter PDFs via platform:
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020 # # |a 9781609601676 (ebook) 
020 # # |z 9781609601652 (hardcover) 
024 7 # |a 10.4018/978-1-60960-165-2  |2 doi 
035 # # |a (CaBNVSL)gtp00550217 
035 # # |a (OCoLC)764494192 
040 # # |a CaBNVSL  |c CaBNVSL  |d CaBNVSL 
050 # 4 |a Q335  |b .D43 2011e 
082 0 4 |a 006.3  |2 22 
245 0 0 |a Decision theory models for applications in artificial intelligence  |b concepts and solutions /  |c L. Enrique Sucar, Eduardo F. Morales and Jesse Hoey, editors.  |h [electronic resource] : 
260 # # |a Hershey, Pa. :  |b IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA),  |c 2011. 
300 # # |a electronic texts (384 p.) :  |b digital files. 
504 # # |a Includes bibliographical references. 
505 0 # |a 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. Díez 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 / Aurélie Beynier and Abdel-Illah Mouaddib. 
506 # # |a Restricted to subscribers or individual electronic text purchasers. 
520 3 # |a "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"--  |c Provided by publisher. 
530 # # |a Also available in print. 
538 # # |a Mode of access: World Wide Web. 
588 # # |a Description based on title screen (IGI Global, viewed November 19, 2011). 
650 # 0 |a Artificial intelligence  |x Statistical methods. 
650 # 0 |a Bayesian statistical decision theory. 
653 # # |a Active learning simulators 
653 # # |a Bayesian networks 
653 # # |a Decision theoretic models 
653 # # |a Health in the home 
653 # # |a Dynamic decision networks applications 
653 # # |a Markov decision processes 
653 # # |a Intelligent assistants 
653 # # |a Power plant operations 
653 # # |a Multistage stochastic programming 
653 # # |a Reinforcement learning 
653 # # |a Semi-Markov decision processes 
653 # # |a Task coordination for service robots 
700 1 # |a Sucar, L. Enrique,  |d 1957- 
700 1 # |a Morales, Eduardo F. 
700 1 # |a Hoey, Jesse. 
710 2 # |a IGI Global. 
776 0 # |c (Original)  |w (DLC)2010054421 
776 0 8 |i Print version:  |w (DLC) 2010054421  |z 9781609601652  |z 1609601653 
856 4 1 |u https://ezaccess.library.uitm.edu.my/login?url=http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-60960-165-2  |3 Chapter PDFs via platform: 

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