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
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Systems and Control
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
Externí odkaz:	http://arxiv.org/abs/2405.14411 Zobrazit plný text záznamu View this record from Arxiv