Policy Explanation and Model Refinement in Decision-Theoretic Planning

Policy Explanation and Model Refinement in Decision-Theoretic Planning

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Decision-theoretic systems, such as Markov Decision Processes (MDPs), are used for sequential decision-making under uncertainty. MDPs provide a generic framework that can be applied in various domains to compute optimal policies. This thesis presents techniques that offer explanations of optimal pol...

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Bibliographic Details
Main Author: Khan, Omar Zia
Language:en
Published: 2013
Subjects:
Decision-Theoretic Planning
Markov Decision Processes
Sequential Decision Making
Reasoning under Uncertainty
Bayesian Networks
Policy Explanation
Parameter Learning
Model Refinement
Computer Science
Online Access:http://hdl.handle.net/10012/7808

Internet

http://hdl.handle.net/10012/7808

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