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Analysis of Bayesian anytime inference algorithms

This dissertation explores and analyzes the performance of several Bayesian
anytime inference algorithms for dynamic influence diagrams. These algorithms are
compared on the On-Line Maintenance Agent testbed, a software artifact permitting
comparison of dynamic reasoning algorithms used by an agent on a variety of simulated
maintenance and monitoring tasks. Analysis of their performance suggests that a
particular algorithmic property, which I term sampling kurtosis, may be responsible for
successful reasoning in the tested half-adder domain. A new algorithm is devised and
evaluated which permits testing of sampling kurtosis, revealing that it may not be the
most significant algorithm property but suggesting new lines of inquiry. Peculiarities in
the observed data lead to a detailed analysis of agent-simulator interaction, resulting in an
equation model and a Stochastic Automata Network model for a random action
algorithm. The model analyses are extended to show that some of the anytime reasoning
algorithms perform remarkably near optimally. The research suggests improvements for
the design and development of reasoning testbeds. / Graduation date: 2002

Identiferoai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/29631
Date31 August 2001
CreatorsBurgess, Scott Alan
ContributorsD' Ambrosio, Bruce D.
Source SetsOregon State University
Languageen_US
Detected LanguageEnglish
TypeThesis/Dissertation

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