Decision making under uncertainty

Thesis

Operating and interacting in an environment requires the ability to manage uncertainty and to choose definite courses of action. In this thesis we look to Bayesian probability theory as the means to achieve the former, and find that through rigorous application of the rules it prescribes we can, in theory, solve problems of decision making under uncertainty. Unfortunately such methodology is intractable in realworld problems, and thus approximation of one form or another is inevitable.

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Authors: McInerney, R

• Publication date: 2014
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: optimal decision making; Gaussian Process; approximate inference; inference; artificial intelligence; decision making; Reinforcement Learning; uncertainty; Bayesian; probability theory; Multi-armed Bandit; machine learning; managing uncertainty
• Subjects: Probability; Robotics; Engineering & allied sciences; Stochastic processes; Artificial Intelligence; Information engineering; Probability theory and stochastic processes; Applications and algorithms; Computing