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Auflistung nach Autor:in "Luttermann, Malte"

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  • Zeitschriftenartikel
    Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain
    (KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Luttermann, Malte; Baake, Edgar; Bouchagiar, Juljan; Gebel, Benjamin; Grüning, Philipp; Manikwadura, Dilini; Schollemann, Franziska; Teifke, Elisa; Rostalski, Philipp; Möller, Ralf
    Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients.
  • Zeitschriftenartikel
    AutoRAG: Grounding Text and Symbols
    (KI - Künstliche Intelligenz: Vol. 38, No. 3, 2024) Schulz, Tim; Luttermann, Malte; Möller, Ralf
    In safety critical domains such as the healthcare domain, systems for natural language question answering demand special correctness guarantees. Modeling problem domains formally allows for automatic transparent reasoning, but handling comprehensive formal models may quickly demand expert knowledge. Ultimately, we need a system which is as easily accessible as large language models while the correctness of its output should be checkable using trusted knowledge. Since words are ambiguous in general but concepts of a formal model are not, we propose to expand the vocabulary of a language model by concepts of a knowledge base: Motivated by retrieval-augmented generation, we introduce AutoRAG, which does not retrieve data from external sources, rather it perceives parts of the knowledge base from special vocabulary, trained by auto-encoding text and concepts. Our AutoRAG implementation for a use case in the field of nosocomial pneumonia describes concepts it associates with the input and can naturally provide a graphical depiction from the expert-made knowledge bas to allow for feasible text sanity checks.