This article presents an AI-enabled Personalization Management (AIIM) software for human-in-the-loop, human-in-the-plant learning-enabled systems (LES). AIIM can be integrated with LES software to aid a human user in achieving safe and effective operation under dynamically changing contexts. AIIM consists of: (a) an AI technique to derive model coefficient of a physics-guided surrogate model from operational data shared following privacy norms, and (b) continuous model conformance to identify key changes in LES operational behavior that may jeopardize safety. We demonstrate two capabilities of AIIM, personalization and unknown error detection, through case studies that span a significant breadth of dynamic context change scenarios including (a) involuntary change in user context such as medication-induced glucose metabolism change in automated insulin delivery (AID), (b) actuation failure such as cartridge blockage in AID, (c) latent sensor error in aviation, and (d) unknown coding error in autonomous car software patches. We compare AIIM personalization with human-in-the-loop and self-adaptive model-predictive control design in real-life and simulation settings, to show safe and improved diabetes management.

Safety regulation of safety-critical agent-based cyber-physical systems (CPS) which are manufactured in large scale such as next-gen aircrafts, autonomous driving vehicles, and medical devices is a multifaceted problem. CPS deployments can be presented with new safety-critical scenarios and novel inputs. Hence, operational characteristics of the CPS can be quite different from its safety approved design. This article considers a safety assurance solution where operational data from the sensors and actuators in the field of deployment is fed back to the manufacturing process through the Internet of Things infrastructure to assure and improve operational safety. It considers two cases: 1) model-aware, where the safety assured CPS design is fully specified; 2) modelagnostic, where limited specifications exist. For both the cases, it presents a data science based approach, N-HyMn, that learns a hybrid automaton model of the operational characteristics of the CPS from the input/output (I/O) traces of the observable parameters. For the model-aware case, it investigates the presence of inconsistencies between the learned model and the specifications model provided by the manufacturer, thus facilitating the detection of safety problems that may have been overlooked. For the modelagnostic case, it can detect potential safety failures. We show the usage of N-HyMn on the Medtronic Minimed 670 G system. N-HyMn correctly infers the hybrid automaton specifications of the Minimed 670 G and was able to detect a self-adaptation mechanism that is not declared explicitly in the certification documents of the U Food and Drug Administration. N-HyMn has a computational complexity of O(kn2), where k is the number of samples in the I/O trace, and n is the number of continuous variables.

AI-enabled Cyber-physical systems (CPS) such as artificial pancreas (AP) or autonomous cars are using machine learning to make several critical decisions. The system is subject to inputs and scenarios which are not observed during training and the expected outputs are not known. Hence, popular model based verification techniques that characterize behavior of a control system before deployment using predictive models may be inaccurate and often result in incorrect safety analysis results. In addition, regulatory agencies are required to regulate safety-critical AI enabled CPS to ensure their operational safety. However, high complexity of the system result in myriad of safety concerns all of which may not only be comprehensively tested before deployment but also may not even be detected during design and testing phase. In this work, we propose a tool to help regulatory agencies compare the operation of the CPS with the specifications given by the manufacturer to ensure that the operation results conform with the safety assured design of a CPS.