AI Simulation by Digital Twins: Systematic Survey of the State of the Art and a Reference Framework

Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation recommends developing virtual training environments in which AI agents can be safely and efficiently developed. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this paper, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Finally, we identify challenges and research opportunities for prospective researchers.