Large Language Models for Explainable Decisions in Dynamic Digital Twins
Autor: | Zhang, Nan, Vergara-Marcillo, Christian, Diamantopoulos, Georgios, Shen, Jingran, Tziritas, Nikos, Bahsoon, Rami, Theodoropoulos, Georgios |
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Rok vydání: | 2024 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: 8 pages, 3 figures, under review |
Databáze: | arXiv |
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