@incollection{ruchkin_confidence_2021,
	address = {Cham},
	title = {Confidence {Monitoring} and {Composition} for {Dynamic} {Assurance} of {Learning}-{Enabled} {Autonomous} {Systems}},
	isbn = {978-3-030-87348-6},
	abstract = {Design-time approaches to safety assurance for autonomous systems are limited because they must rely on assumptions about the behaviors of learned components in previously unseen environments. These assumptions may be violated at run time, thus invalidating the guarantees produced at design time. To overcome this limitation, we propose to complement design-time assurance with run-time monitoring that calculates the confidence that those assumptions are satisfied and, therefore, design-time guarantees continue to hold. As the first step in our vision, we elicit the logical relationship between assumption violations and safety violations. Then, we develop a probabilistic confidence monitor for each design-time assumption. Finally, we compose these assumption monitors based on their logical relation to safety violations, producing a system-wide assurance monitor. Our vision is illustrated with a case study of an autonomous underwater vehicle that performs pipeline inspection.},
	language = {en},
	urldate = {2021-10-23},
	booktitle = {Formal {Methods} in {Outer} {Space}: {Essays} {Dedicated} to {Klaus} {Havelund} on the {Occasion} of {His} 65th {Birthday}},
	publisher = {Springer International Publishing},
	author = {Ruchkin, Ivan and Cleaveland, Matthew and Sokolsky, Oleg and Lee, Insup},
	year = {2021},
	doi = {10.1007/978-3-030-87348-6_8},
	pages = {137--146},
	file = {Springer Full Text PDF:/home/ivan/Dropbox/configs/zotero_storage/storage/W49ZT3CC/Ruchkin et al. - 2021 - Confidence Monitoring and Composition for Dynamic .pdf:application/pdf},
}