Enabling interoperable human–AI teaming for automation in construction and manufacturing via Digital Twins and Sliding Work Sharing ontologies

This paper introduces an ontology system to support dynamic, explainable, and human-centric collaboration between humans and artificial intelligence-enabled non-human agents in cyber–physical environments. In this setting, Digital Twins (digital models of physical systems or processes that mirror their real-time state) and Human Digital Twins (digital representations of individual humans, including their physiological or cognitive states) may provide information to enable an appropriate dynamic allocation of the work that can be shared by humans and AI actors (i.e., sliding work sharing). A novel upper-level Sliding Work Sharing ontology is defined to support semantic interoperability and reasoning across diverse domains, facilitating sliding work sharing in complex environments. The ontology is grounded in Industry 5.0 concepts and built upon the Industrial Ontology Foundry core ontology. It extends conventional scheduling ontologies by incorporating key constructs for Digital Twins, Human Digital Twins, and dynamic task flows. We validate the ontology through two use cases from the domains of automation in construction and manufacturing. The collaborative construction case involves robots and humans, while the manufacturing one integrates legacy systems, artificial intelligence actors, and human planners. The developed ontology system is evaluated for its coverage and expressiveness through a novel Retrieval-Augmented Generation based methodology, applied on diverse Large Language Models to derive competency questions from external sources. This approach enhances conventional ontology validation techniques with a scalable and unbiased alternative. Logical consistency is confirmed using a range of standard reasoners. Our results demonstrate that the Sliding Work Sharing ontology has considerable flexibility and potential to advance human–AI teaming in future work environments. • Digital Twins and Sliding Work Sharing ontologies. • Large language model retrieval augmented generation for ontology evaluation. • Human AI-teaming in construction and manufacturing automation.