Analysing the synergies between Multi-agent Systems and Digital Twins: A systematic literature review

• Published in: Information and software technology Vol. 174; p. 107503
• Main Authors: Pretel, Elena, Moya, Alejandro, Navarro, Elena, López-Jaquero, Víctor, González, Pascual
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2024
• Subjects: Digital twin; Internet of Things; Literature review; MAS; Multi-agent system; Literature review; Digital twin; Multi-agent system; Internet of Things; MAS
• ISSN: 0950-5849; 1873-6025
• DOI: 10.1016/j.infsof.2024.107503

Digital Twins (DTs) are used to augment physical entities by exploiting assorted computational approaches applied to the virtual twin counterpart. A DT is generally described as a physical entity, its virtual counterpart, and the data connections between them. Multi-Agent Systems (MAS) paradigm is alike DTs in many ways. Agents of MAS are entities operating and interacting in a specific environment, while exploring and collecting data to solve some tasks. This paper presents the results of a systematic literature review (SLR) focused on the analysis of current proposals exploiting the synergies of DTs and MAS. This research aims to synthesize studies that focus on the use of MAS to support DTs development and MAS that exploit DTs, paving the way for future research. A SLR methodology was used to conduct a detailed study analysis of 64 primary studies out of a total of 220 studies that were initially identified. This SLR analyses three research questions related to the synergies between MAS and DT. The most relevant findings of this SLR and their implications for further research are the following: i) most of the analyzed proposals design digital shadows rather than DT; ii) they do not fully support the properties expected from a DT; iii) most of the MAS properties have not fully exploited for the development of DT; iv) ontologies are frequently used for specifying semantic models of the physical twin. Based on the results of this SLR, our conclusions for the community are presented in a research agenda that highlights the need of innovative theoretical proposals and design frameworks that guide the development of DT. They should be defined exploiting the properties of MAS to unleash the full potential of DT. Finally, ontologies for machine learning models should be designed for its use in DT.