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.

利用大型语言模型为动态数据驱动型数字孪生提供可解释性平台，利用领域专业知识库生成系统决策的自然语言解释。以智能农业为案例进行了研究。

大型语言模型用于动态数字孪生中的可解释决策

Large Language Models for Explainable Decisions in Dynamic Digital Twins

Digital Twins (DT) are essentially dynamic data-driven models that serve as
real-time symbiotic "virtual replicas" of real-world systems. DT can leverage
fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional
symbiotic sensing feedback loops for its continuous updates. Sensing loops can
consequently steer measurement, analysis and reconfiguration aimed at more
accurate modelling and analysis in DT. The reconfiguration decisions can be
autonomous or interactive, keeping human-in-the-loop. The trustworthiness of
these decisions can be hindered by inadequate explainability of the rationale,
and utility gained in implementing the decision for the given situation among
alternatives. Additionally, different decision-making algorithms and models
have varying complexity, quality and can result in different utility gained for
the model. The inadequacy of explainability can limit the extent to which
humans can evaluate the decisions, often leading to updates which are unfit for
the given situation, erroneous, compromising the overall accuracy of the model.
The novel contribution of this paper is an approach to harnessing
explainability in human-in-the-loop DDDAS and DT systems, leveraging
bidirectional symbiotic sensing feedback. The approach utilises interpretable
machine learning and goal modelling to explainability, and considers trade-off
analysis of utility gained. We use examples from smart warehousing to
demonstrate the approach.

本文提出了一种利用可解释机器学习和目标建模的方法，以加强人在环路动态数据驱动的应用系统和数字孪生系统的可解释性，并考虑效用的权衡分析。实验结果表明，该方法在智能仓储的运用中取得了成功。