Digital twins as self-models for intelligent structures.
Journal:
Scientific reports
Published Date:
Aug 19, 2025
Abstract
A self-model is an artificial intelligence that is able to create a continuously updated internal representation of itself. In this paper we use an agent-based architecture to create a 'digital twin self-model', using the example of a small-scale three-story building. The architecture is based on a set of heterogeneous digital components, each managed by an agent. The agents can be orchestrated to perform a specific workflow, or collaborate with a human user to perform requested tasks. The digital twin architecture enables multiple complex behaviors to be represented via a time-evolving dynamic assembly of the digital components, that also includes the encoding of a self-model in a knowledge graph as well as producing quantitative outputs. Four operational modes are defined for the digital twin and the example shown here demonstrates an offline mode that executes a predefined workflow with five agents. The digital twin has an information management system which is coordinated using a dynamic knowledge graph that encodes the self-model. Users can visualize the knowledge graph via a web-based user interface and also input natural language queries. Retrieval augmented generation is used to give a response to the queries using both the local knowledge graph and a large language model.
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