Large Language Models for Explainable Decisions in Dynamic Digital Twins
<|reference_start|>Large Language Models for Explainable Decisions in Dynamic Digital Twins: 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.<|reference_end|>
@article{zhang2024large,
    title={Large Language Models for Explainable Decisions in Dynamic Digital Twins},
    author={Nan Zhang, Christian Vergara-Marcillo, Georgios Diamantopoulos,
  Jingran Shen, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos},
    journal={arXiv preprint arXiv:2405.14411},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14411},
    primaryClass={cs.AI cs.SY eess.SY}
}