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Towards a Framework for Safety Assurance of Autonomous Systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Publication details

Title of host publicationArtificial Intelligence Safety 2019
DatePublished - 11 Aug 2019
Pages1-7
Number of pages8
PublisherCEUR Workshop Proceedings
EditorsHuascar Espinoza, Han Yu, Xiaowei Huang, Freddy Lecue, Cynthia Chen, Jose Hernandex-Orallo, Sean o hEigeartaigh, Richard Mallah
Volume2419
Original languageEnglish
ISBN (Electronic)1613-0073

Abstract

Autonomous systems have the potential to provide great benefit to society. However, they also pose problems for safety assurance, whether fully auton-omous or remotely operated (semi-autonomous). This paper discusses the challenges of safety assur-ance of autonomous systems and proposes a novel framework for safety assurance that, inter alia, uses machine learning to provide evidence for a system safety case and thus enables the safety case to be updated dynamically as system behaviour evolves.

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