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。