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Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
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Digital twins as self-models for intelligent structures.

Xiaoxue Shen1, David J Wagg2,3, Matthew Tipuric1,4

  • 1The Alan Turing Institute, London, NW1 2DB, UK.

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|August 19, 2025
PubMed
Summary

This study introduces a digital twin self-model using an agent-based architecture for artificial intelligence. This AI creates an internal representation of itself, enabling complex behaviors and user interaction through a knowledge graph.

Keywords:
AgentDigital twinSelf-modelStructure

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Digital Twins

Background:

  • Self-models in AI are crucial for self-awareness and adaptation.
  • Existing architectures may lack dynamic, integrated self-representation.
  • Agent-based systems offer modularity for complex AI construction.

Purpose of the Study:

  • To develop a 'digital twin self-model' using an agent-based architecture.
  • To represent complex AI behaviors through dynamic assembly of digital components.
  • To integrate a self-model within a knowledge graph for dynamic information management.

Main Methods:

  • An agent-based architecture with heterogeneous digital components was employed.
  • A knowledge graph was utilized to encode the self-model and manage information.
  • Retrieval augmented generation (RAG) combined a local knowledge graph with a large language model for query responses.
  • A web-based user interface was developed for visualization and natural language querying.

Main Results:

  • A functional digital twin self-model was demonstrated using a small-scale building example.
  • The architecture supports multiple operational modes, including an offline workflow execution.
  • The dynamic knowledge graph successfully encoded the self-model and facilitated information retrieval.
  • Natural language queries were processed effectively using RAG.

Conclusions:

  • The agent-based digital twin architecture provides a robust framework for creating self-aware AI.
  • Dynamic knowledge graphs are effective for encoding and managing self-model information.
  • Integration with large language models enhances the interactive capabilities of AI self-models.