Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. By leveraging principles of Dynamic Data-Driven Applications Systems (DDDAS), DDTs can formulate computational modalities for feedback loops, model updates and decision-making, including autonomous ones. However, understanding autonomous decision-making often requires technical and domain-specific knowledge. This paper explores using large language models (LLMs) to provide an explainability platform for DDTs, generating natural language explanations of the system's decision-making by leveraging domain-specific knowledge bases. A case study from smart agriculture is presented.
@article{arxiv.2405.14411,
title = {Large Language Models for Explainable Decisions in Dynamic Digital Twins},
author = {Nan Zhang and Christian Vergara-Marcillo and Georgios Diamantopoulos and Jingran Shen and Nikos Tziritas and Rami Bahsoon and Georgios Theodoropoulos},
journal= {arXiv preprint arXiv:2405.14411},
year = {2024}
}
Comments
9 pages, 3 figures, accepted by DDDAS2024 -- the 5th International Conference on Dynamic Data Driven Applications Systems