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 that provides context-specific support to manufacturing operators. In the frame of the EU funded Teaming.AI project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a reference software architecture based on knowledge graphs, tracking and scene analysis, and components for relational machine learning with a particular focus on its scalability. Our approach uses knowledge graphs to capture product- and process specific knowledge in the manufacturing process and to utilize it for relational machine learning. This allows for context-specific recommendations for actions in the manufacturing process for the optimization of product quality and the prevention of physical harm. The empirical validation of this software architecture will be conducted in cooperation with three large-scale companies in the automotive, energy systems, and precision machining domain. In this paper we discuss the identified challenges for such a reference software architecture, present its preliminary status, and sketch our further research vision in this project.
翻译:在欧盟资助的Teaming.AI项目框架内,我们确定监测人类-AI协作中的团队合作方面、运行时间监测和道德政策的验证、以及支持数据和机器学习算法实验是智能制造中人类-AI团队合作的最相关挑战。根据这些挑战,我们开发了一个参考软件结构,其基础是知识图、跟踪和场景分析,以及关联机器学习的组成部分,特别侧重于其可扩展性。我们的方法使用知识图表来捕捉制造过程中的产品和工艺方面的具体知识,并将之用于关联机器学习。这样就可以就制造过程中优化产品质量和防止物理伤害的行动提出具体背景建议。这一软件结构的实证验证将与汽车、能源系统和精密编程域的三家大型公司合作进行。在本文件中,我们进一步讨论了这一参考软件结构的挑战,介绍了其初步状况,以及我们这一愿景项目中的素描图。