Large Language Models for Explainable Decisions in Dynamic Digital Twins

2024/05/23 by Nan Zhang, Zhang, Nan, Christian Vergara-Marcillo +11 · 2 citations
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Electrical engineering #Scientific Computing and Data Management #Systems and Control (eess.SY) #Topic Modeling #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2405.14411

openalex publication_date 2024/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Advancing Security with Digital Twins: A Comprehensive Survey
• Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making

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