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Large Language Models for Explainable Decisions in Dynamic Digital Twins

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arxiv 2405.14411 v2 pith:OH4WBW3Q submitted 2024-05-23 cs.AI cs.SYeess.SY

Nan Zhang , Christian Vergara-Marcillo , Georgios Diamantopoulos , Jingran Shen , Nikos Tziritas , Rami Bahsoon , Georgios Theodoropoulos This is my paper
classification cs.AIcs.SYeess.SY
keywords decision-makingddtsdynamiclanguageautonomousdata-drivendigitaldomain-specific
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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