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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2405.14411 |
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| _version_ | 1866916381464723456 |
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| author | Zhang, Nan Vergara-Marcillo, Christian Diamantopoulos, Georgios Shen, Jingran Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios |
| author_facet | Zhang, Nan Vergara-Marcillo, Christian Diamantopoulos, Georgios Shen, Jingran Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14411 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Explainable Decisions in Dynamic Digital Twins Zhang, Nan Vergara-Marcillo, Christian Diamantopoulos, Georgios Shen, Jingran Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios Artificial Intelligence Systems and Control 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. |
| title | Large Language Models for Explainable Decisions in Dynamic Digital Twins |
| topic | Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2405.14411 |