Towards Integrating Knowledge Graphs into Process-Oriented Human-AI Collaboration in Industry

Human-AI collaboration in industrial manufacturing promises to overcome current limitations by combining the flexibility of human intelligence and the scaling and processing capabilities of machine intelligence. To ensure effective collaboration between human and AI team members, we envision a software-driven coordination mechanism that orchestrates the interactions between the participants in Human-AI teaming scenarios and help to synchronize the information flow between them. A structured process-oriented approach to systems engineering aims at generalizability, deployment efficiency and enhancing the quality of the resulting software by formalizing the human-AI interaction as a BPMN process model. During runtime, this process model is executed by the teaming engine, one of the core components of the Teaming.AI software platform. By incorporating dynamic execution traces of these process models into a knowledge graph structure and linking them to contextual background knowledge, we facilitate the monitoring of variations in process executions and inference of new insights during runtime. Knowledge graphs are a powerful tool for semantic integration of diverse data, thereby significantly improving the data quality, which is still one of the biggest issues in AI-driven software solutions. We present the Teaming.AI software platform and its key components as a framework for enabling transparent teamwork between humans and AI in industry. We discuss its application in the context of an industrial use case in plastic injection molding production. Overall, this Teaming.AI platform provides a robust, flexible and accountable solution for human-AI collaboration in manufacturing.