Elicitation of Executable Safety Rules for Critical Autonomous Systems

International audienceThe progress of artificial intelligence techniques, particularly decisional mechanisms, has allowed reactive systems to become more autonomous. This allows new applications in domains such as service robotics in which failures can lead to human injury or death, or financial loss. To ensure safety of such systems, we propose in this paper a process, based on a HAZOP/UML risk analysis, to elicit safety rules that can be enforced on-line. We present a case study of safety rule elicitation for an assistive robot for strolling and discuss implementation of the safety rules in a practical safety monitor