Large Language Models for Explainable Decisions in Dynamic Digital Twins
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2024
- DOI:
- 10.48550/arXiv.2405.14411
- arXiv:
- arXiv:2405.14411
- Bibcode:
- 2024arXiv240514411Z
- Keywords:
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- Computer Science - Artificial Intelligence;
- Electrical Engineering and Systems Science - Systems and Control
- E-Print:
- 8 pages, 3 figures, under review