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Head of Research Department Plan-Based Robot Control

Prof. Dr. Martin Atzmüller

Organizational unit Plan-Based Robot Control
Contact +49 541 386050 2287 (Osnabrück)

Address (Osnabrück)

Publications

SHAP value-based ERP analysis (SHERPA): Increasing the sensitivity of EEG signals with explainable AI methods

Sophia Sylvester; Merle Sagehorn; Thomas Gruber; Martin Atzmueller; Benjamin Schöne

In: Behavior Research Methods, Pages 1-15, Springer, 2024.

To the publication

A Neuro-Symbolic Approach for Anomaly Detection and Complex Fault Diagnosis Exemplified in the Automotive Domain

Tim Bohne; Anne-Kathrin Patricia Windler; Martin Atzmueller

In: Brent Venable; Daniel Garijo; Brian Jalaian (Hrsg.). Proceedings of the 12th Knowledge Capture Conference 2023. International Conference on Knowledge Capture (K-Cap-2023), December 5-7, Pensacola, FL, USA, Pages 35-43, K-CAP '23, Vol. 12, ISBN 979-8-4007-0141-2, Association for Computing Machinery, New York, NY, USA, 12/2023.

To the publication

Enhanced Explanations for Knowledge-Augmented Clustering using Subgroup Discovery

Maciej Szelążek; Dan Hudson; Szymon Bobek; Grzegorz J. Nalepa; Martin Atzmueller

In: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA). International Conference on Data Science and Advanced Analytics (DSAA-2023), October 9-13, Thessaloniki, Greece, Pages 1-11, IEEE, 11/2023.

To the publication
All publications

Profile

  • ExPrIS

    Knowledge-Level Expectations as Priors for Object Interpretation from Sensor Data

    "What will I see when I turn this corner?"

    Depending on what is around that corner, this question is easy for us to answer. We are likely to see a refrigerator in a kitchen, ships at a port, and a…

    ExPrIS

  • AW4.0

    Autowerkstatt4.0

    In the project Autowerkstatt 4.0 (AW 4.0), a consortium consisting of companies and research institutes is developing a platform for the trustworthy data exchange of industry-specific AI models and…

    AW4.0

  • AI-Test-Field

    Experimental environment for industrial-grade development of semantic environment perception.

    The fusion of data with high spatial and temporal resolution and their interpretation are essential innovation drivers for the realisation of more sustainable processes in plant cultivation. Here,…

    AI-Test-Field