Analysis of Bayesian anytime inference algorithms Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9019s534g

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  • 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.
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