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Title:Application of black-box optimization on system verification
Author(s):Leung, Peter
Contributor(s):Mitra, Sayan
Optimistic Optimization
Abstract:System verification is a very generalizable problem about ensuring how a system behaves. Reliable system verification techniques are essential as autonomous systems are becoming more prevalent. This can be seen with increasing numbers of self-driving cars and drone deliveries. Our research approaches this problem by determining if a system is safe. In other words: Given the possible states from different initial conditions, do all these states avoid a predefined unsafe set? We have set up a method to utilize optimization techniques to help verify systems. Our research focuses on black-box verification, where we can only take zero-order evaluations of the function in question. There is already much research into problems where we have full knowledge of the function to be optimized, or no knowledge at all. But limited work has been done in the area in between. We hope to leverage some knowledge of the “smoothness” of the function to help us verify systems. This thesis explains how we set up these systems and utilized previous research on optimistic optimization. The thesis proposes a new optimization algorithm. Lastly results are compared to suggest what systems and conditions would be best suited to each algorithm, or whether an approach entirely different from ours would be more appropriate.
Issue Date:2020-05
Date Available in IDEALS:2020-06-11

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