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Explainable Human-in-the-Loop Dynamic Data-Driven Digital Twins

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Dynamic Data Driven Applications Systems (DDDAS 2022)

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Abstract

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.

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Acknowledgements

This research was supported by: Shenzhen Science and Technology Program, China (No. GJHZ20210705141807022); SUSTech-University of Birmingham Collaborative PhD Programme; Guangdong Province Innovative and Entrepreneurial Team Programme, China (No. 2017ZT07X386); SUSTech Research Institute for Trustworthy Autonomous Systems, China; and EPSRC/EverythingConnected Network project on Novel Cognitive Digital Twins for Compliance, UK.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China

    Nan Zhang & Georgios Theodoropoulos

  2. School of Computer Science, University of Birmingham, Birmingham, UK

    Nan Zhang & Rami Bahsoon

  3. Research Institute for Trustworthy Autonomous Systems, Shenzhen, China

    Georgios Theodoropoulos

  4. Department of Informatics and Telecommunications, University of Thessaly, Volos, Greece

    Nikos Tziritas

Authors
  1. Nan Zhang
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  2. Rami Bahsoon
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  3. Nikos Tziritas
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  4. Georgios Theodoropoulos
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Corresponding author

Correspondence to Georgios Theodoropoulos .

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Editors and Affiliations

  1. MOVEJ Analytics, Fairborn, OH, USA

    Erik Blasch

  2. InfoSymbiotic Systems Society, Bethesda, MD, USA

    Frederica Darema

  3. Air Force Research Laboratories, Canastota, NY, USA

    Alex Aved

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Zhang, N., Bahsoon, R., Tziritas, N., Theodoropoulos, G. (2024). Explainable Human-in-the-Loop Dynamic Data-Driven Digital Twins. In: Blasch, E., Darema, F., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2022. Lecture Notes in Computer Science, vol 13984. Springer, Cham. https://doi.org/10.1007/978-3-031-52670-1_23

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  • DOI: https://doi.org/10.1007/978-3-031-52670-1_23

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  • Online ISBN: 978-3-031-52670-1

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