AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues
<|reference_start|>AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues: This paper describes the capabilities and potential of the intelligent personal assistant (IPA) CORE (Checklist Organizer for Research and Exploration), designed to support astronauts during procedures onboard the International Space Station (ISS), the Lunar Gateway station, and beyond. We reflect on the importance of a reliable and flexible assistant capable of offline operation and highlight the usefulness of audiovisual interaction using augmented reality elements to intuitively display checklist information. We argue that current approaches to the design of IPAs in space operations fall short of meeting these criteria. Therefore, we propose CORE as an assistant that combines Knowledge Graphs (KGs), Retrieval-Augmented Generation (RAG) for a Generative Pre-Trained Transformer (GPT), and Augmented Reality (AR) elements to ensure an intuitive understanding of procedure steps, reliability, offline availability, and flexibility in terms of response style and procedure updates.<|reference_end|>
@article{bensch2024ai,
    title={AI Assistants for Spaceflight Procedures: Combining Generative
  Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge
  Graphs With Augmented Reality Cues},
    author={Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling, Bernd
  Bewer, Sophie Jentzsch, Tobias Hecking, J. Nathan Kutz},
    journal={arXiv preprint arXiv:2409.14206},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14206},
    primaryClass={cs.AI cs.HC}
}