This module addresses digital twins for activities on resources.
The module introduces a methodology to identify the real-world counterparts for digital twins. The approach supports incremental development. Digital twins can be added and/or enhanced in steps as progressive insights indicate. Use cases identify what is relevant in the world of interest, which the digital twins will mirror. The digital twins avoid introducing constraints that are absent in the corresponding reality, which yields in-depth interoperability and integrate-ability.
The methodology applies the ARTI reference architecture to account for bounded rationality. Indeed, the development of digital twins requires time and effort. Without a suitable architecture, digital twins will be obsolete before they achieve sufficient maturity and stability. ARTI provides guidance, allowing to delineate which part of reality is to be covered by each digital twin. And, ARTI reveals how to aggregate twins such that an ever-changing world-of-interest can be tracked and mirrored by the twins with as little effort as possible.
On a more advanced level, digital twins of mental states (i.e. intentions) are introduced. This can be simply “first come, first served” or may need some machine learning to estimate how an operator behaves. Importantly, the combination of ARTI twins with intentions allows generating predictions. It yields a situation awareness that covers the past (trace), the present (track) and the future (i.e. computes what happens when everyone executes their intentions).
Finally, the module illustrate how services and functionality can build upon a web of digital twins, and why they shall prefer to use digital twins over direct interactions among themselves. The discussion also covers the integration of specialist or high-tech digital twins in this network of everyday digital twins.