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Autonomous systems have the potential to provide great benefit to society. However, they also pose problems for safety assurance, whether fully autonomous or remotely operated (semi-autonomous). This paper discusses the challenges of safety assurance 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.dblp:conf/ijcai/McDermidJH19 fatcat:5zfjqsfiendqvbe3dcend7522m