We are surrounded by a variety of more-or-less intelligent technical devices, designed to serve you us or others. Applications onin your mobile phones, wrist-worn health sensors on your wrists, autonomous vacuum cleaners, robots on the factory floor and increasingly autonomous cars – all pledge to ease your tasks and keep usyou safe and healthy. SThe seamless interplay with these devices gets gainsmore importane as these devices proliferate and grow in t with the increased autonomy and pervasive presence of the devices. We expect continuously available support fromin the services they provide − yet we want them to disappear unobtrusively in the background when not needed. In order to provide support in a collaborative environment with human, physical and digital players, the technology needs to be equipped with senses to grasp human presence, their mental and physical state, their activities and their intentions. This is required to ensure human safety, safeguard their health, and allow for natural interaction. This project intends to improve sensing of human presence, behaviour and health in a collaborative or common environment by means of multi-sensor systems.

The project idea is focusing on AI-augmented automation supporting modeling, coding, testing, and monitoring as part of a continuous development in Cyber-Physical Systems (CPSs). The growing complexity of CPS poses several challenges throughout all software development and analysis phases, but also during their usage and maintenance. Many leading companies have started envisaging the automation of tomorrow to be brought about by Artificial Intelligence (AI) tech. While the number of companies that invest significant resources in software development is constantly increasing, the use of AI in the development and design techniques is still immature. The project targets the development of a model-based framework to support teams during the automated continuous development of CPSs by means of integrated AI-augmented solutions. The overall AIDOaRT infrastructure will work with existing data sources, including traditional IT monitoring, log events, along with software models and measurements. The infrastructure is intended to operate within the DevOps process combining software development and information technology (IT) operations. Moreover, AI technological innovations have to ensure that systems are designed responsibly and contribute to our trust in their behaviour (i.e., requiring both accountability and explainability). AIDOaRT aims to impact organizations where continuous deployment and operations management are standard operating procedures. DevOps teams may use the AIDOaRT framework to analyze event streams in real-time and historical data, extract meaningful insights from events for continuous improvement, drive faster deployments and better collaboration, and reduce downtime with proactive detection.