Large Language Models for Explainable Decisions in Dynamic Digital Twins

• URL: http://arxiv.org/abs/2405.14411v2
• Date: Wed, 4 Sep 2024 06:00:56 GMT
• Title: Large Language Models for Explainable Decisions in Dynamic Digital Twins
• Authors: Nan Zhang, Christian Vergara-Marcillo, Georgios Diamantopoulos, Jingran Shen, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos,
• Abstract summary: Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. This paper explores using large language models (LLMs) to provide an explainability platform for DDTs. It generates natural language explanations of the system's decision-making by leveraging domain-specific knowledge bases.
• Score: 3.179208155005568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Towards an Extensible Model-Based Digital Twin Framework for Space Launch Vehicles [12.153961316909852]
  The concept of Digital Twin (DT) is increasingly applied to systems on different levels of abstraction across domains. The definition of DT is unclear, neither is there a clear pathway to develop DT to fully realise its capacities. We propose a DT maturity matrix, based on which we propose a model-based DT development methodology.
  arXiv  Detail & Related papers  (2024-06-04T11:31:00Z)
• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving [69.82743399946371]
  DriveMLM is a framework that can perform close-loop autonomous driving in realistic simulators. We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system. This model can plug-and-play in existing AD systems such as Apollo for close-loop driving.
  arXiv  Detail & Related papers  (2023-12-14T18:59:05Z)
• LLM4Drive: A Survey of Large Language Models for Autonomous Driving [62.10344445241105]
  Large language models (LLMs) have demonstrated abilities including understanding context, logical reasoning, and generating answers. In this paper, we systematically review a research line about textitLarge Language Models for Autonomous Driving (LLM4AD).
  arXiv  Detail & Related papers  (2023-11-02T07:23:33Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Explainable Data-Driven Optimization: From Context to Decision and Back Again [76.84947521482631]
  Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. We introduce a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.
  arXiv  Detail & Related papers  (2023-01-24T15:25:16Z)
• Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins [6.657586324950896]
  Digital Twins (DT) are essentially Dynamic Data-driven models that serve as real-time symbiotic "virtual replicas" of real-world systems. This paper is an approach to harnessing explainability in human-in-the-loop DDDAS and DT systems, leveraging bidirectional symbiotic sensing feedback.
  arXiv  Detail & Related papers  (2022-07-19T07:15:12Z)
• Decentralized digital twins of complex dynamical systems [0.0]
  We introduce a decentralized twin (DDT) framework for dynamical systems. We discuss the prospects of the DDT paradigm in computational science and engineering applications.
  arXiv  Detail & Related papers  (2022-07-07T19:44:42Z)
• Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
  Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
  arXiv  Detail & Related papers  (2022-05-05T13:40:08Z)
• MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning [50.40151403246205]
  Huge language models (LMs) have ushered in a new era for AI, serving as a gateway to natural-language-based knowledge tasks. We define a flexible architecture with multiple neural models, complemented by discrete knowledge and reasoning modules. We describe this neuro-symbolic architecture, dubbed the Modular Reasoning, Knowledge and Language (MRKL) system.
  arXiv  Detail & Related papers  (2022-05-01T11:01:28Z)
• Provably Robust Model-Centric Explanations for Critical Decision-Making [14.367217955827002]
  We show that data-centric methods may yield brittle explanations of limited practical utility. The model-centric framework, however, can offer actionable insights into risks of using AI models in practice.
  arXiv  Detail & Related papers  (2021-10-26T18:05:49Z)
• DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models [36.50754934147469]
  We exploit the potential of counterfactual explanations to understand and explore the behavior of machine learning models. We design DECE, an interactive visualization system that helps understand and explore a model's decisions on individual instances and data subsets.
  arXiv  Detail & Related papers  (2020-08-19T09:44:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.