| Title: |
Knowledge Equivalence in Digital Twins of Intelligent Systems |
| Authors: |
Zhang, Nan; Bahsoon, Rami; Tziritas, Nikos; Theodoropoulos, Georgios |
| Contributors: |
Shenzhen Science and Technology Program, China; SUSTech-University of Birmingham Collaborative PhD Programme; Guangdong Province Innovative and Entrepreneurial Team Programme, China; SUSTech Research Institute for Trustworthy Autonomous Systems, China; EPSRC/EverythingConnected Network project on Novel Cognitive Digital Twins for Compliance, UK |
| Source: |
ACM Transactions on Modeling and Computer Simulation ; volume 34, issue 1, page 1-37 ; ISSN 1049-3301 1558-1195 |
| Publisher Information: |
Association for Computing Machinery (ACM) |
| Publication Year: |
2024 |
| Description: |
A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous. The article focuses in particular on digital twin models of intelligent systems where the systems are knowledge-aware but with limited capability. The digital twin improves the acting of the physical system at a meta-level by accumulating more knowledge in the simulated environment. The modelling of such an intelligent physical system requires replicating the knowledge-awareness capability in the virtual space. Novel equivalence maintaining techniques are needed, especially in synchronising the knowledge between the model and the physical system. This article proposes the notion of knowledge equivalence and an equivalence maintaining approach by knowledge comparison and updates. A quantitative analysis of the proposed approach confirms that compared to state equivalence, knowledge equivalence maintenance can tolerate deviation thus reducing unnecessary updates and achieve more Pareto efficient solutions for the tradeoff between update overhead and simulation reliability. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1145/3635306 |
| Availability: |
https://doi.org/10.1145/3635306; https://dl.acm.org/doi/10.1145/3635306; https://dl.acm.org/doi/pdf/10.1145/3635306 |
| Rights: |
https://www.acm.org/publications/policies/copyright_policy#Background |
| Accession Number: |
edsbas.1732F33 |
| Database: |
BASE |