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With the proliferation of AI-enabled software systems in smart manufacturing,the role of such systems moves away from a reactive to a proactive role thatprovides context-specific support to manufacturing operators. In the frame ofthe EU funded Teaming.AI project, we identified the monitoring of teamingaspects in human-AI collaboration, the runtime monitoring and validation ofethical policies, and the support for experimentation with data and machinelearning algorithms as the most relevant challenges for human-AI teaming insmart manufacturing. Based on these challenges, we developed a referencesoftware architecture based on knowledge graphs, tracking and scene analysis,and components for relational machine learning with a particular focus on itsscalability. Our approach uses knowledge graphs to capture product- and processspecific knowledge in the manufacturing process and to utilize it forrelational machine learning. This allows for context-specific recommendationsfor actions in the manufacturing process for the optimization of productquality and the prevention of physical harm. The empirical validation of thissoftware architecture will be conducted in cooperation with three large-scalecompanies in the automotive, energy systems, and precision machining domain. Inthis paper we discuss the identified challenges for such a reference softwarearchitecture, present its preliminary status, and sketch our further researchvision in this project.

Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing