Andreas Sedlmeier, M.Sc.

Andreas Sedlmeier, M.Sc.

Lehrstuhl für Mobile und Verteilte Systeme

Ludwig-Maximilians-Universität München, Institut für Informatik

Oettingenstraße 67
80538 München

Raum E 008

Telefon: +49 89 / 2180-9173

Fax: +49 89 / 2180-9148

Mail: andreas.sedlmeier@ifi.lmu.de

Research Interests

  • Artificial Intelligence
  • Machine Learning
  • Uncertainty and Robustness in AI Systems
  • Reinforcement Learning

Publications

2020

  • S. Illium, R. Müller, A. Sedlmeier, and C. Linnhoff-Popien, "Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms," in Proc. Interspeech 2020, 2020, pp. 2052-2056. doi:10.21437/Interspeech.2020-1692
    [BibTeX] [Download PDF]
    @inproceedings{Illium2020,
    author={Steffen Illium and Robert Müller and Andreas Sedlmeier and Claudia Linnhoff-Popien},
    title={{Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms}},
    year=2020,
    booktitle={Proc. Interspeech 2020},
    pages={2052--2056},
    doi={10.21437/Interspeech.2020-1692},
    url={http://dx.doi.org/10.21437/Interspeech.2020-1692}
    }
  • A. Sedlmeier, R. Müller, S. Illium, and C. Linnhoff-Popien, "Policy Entropy for Out-of-Distribution Classification," in Artificial Neural Networks and Machine Learning -- ICANN 2020, Cham, 2020, pp. 420-431.
    [BibTeX] [Abstract]
    One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning. It is based on using the entropy of an agent's policy as the classification score of a one-class classifier. We evaluate our approach using a procedural environment generator. Results show that PEOC is highly competitive against state-of-the-art one-class classification algorithms on the evaluated environments. Furthermore, we present a structured process for benchmarking out-of-distribution classification in reinforcement learning.
    @InProceedings{sedlmeier2020peoc,
    author="Sedlmeier, Andreas
    and M{\"u}ller, Robert
    and Illium, Steffen
    and Linnhoff-Popien, Claudia",
    editor="Farka{\v{s}}, Igor
    and Masulli, Paolo
    and Wermter, Stefan",
    title="Policy Entropy for Out-of-Distribution Classification",
    booktitle="Artificial Neural Networks and Machine Learning -- ICANN 2020",
    year="2020",
    publisher="Springer International Publishing",
    address="Cham",
    pages="420--431",
    abstract="One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Such situations could lead to potential safety risks when wrong predictions lead to the execution of harmful actions. In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning. It is based on using the entropy of an agent's policy as the classification score of a one-class classifier. We evaluate our approach using a procedural environment generator. Results show that PEOC is highly competitive against state-of-the-art one-class classification algorithms on the evaluated environments. Furthermore, we present a structured process for benchmarking out-of-distribution classification in reinforcement learning.",
    isbn="978-3-030-61616-8"
    }
  • T. Phan, T. Gabor, A. Sedlmeier, F. Ritz, B. Kempter, C. Klein, H. Sauer, R. Schmid, J. Wieghardt, M. Zeller, and others, "Learning and testing resilience in cooperative multi-agent systems," in Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020, pp. 1055-1063.
    [BibTeX] [Download PDF]
    @inproceedings{phan2020learning,
    title={Learning and testing resilience in cooperative multi-agent systems},
    author={Phan, Thomy and Gabor, Thomas and Sedlmeier, Andreas and Ritz, Fabian and Kempter, Bernhard and Klein, Cornel and Sauer, Horst and Schmid, Reiner and Wieghardt, Jan and Zeller, Marc and others},
    booktitle={Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
    pages={1055--1063},
    year={2020},
    url = {http://ifaamas.org/Proceedings/aamas2020/pdfs/p1055.pdf},
    owner = {tphan}
    }
  • C. Hahn, T. Phan, S. Feld, C. Roch, F. Ritz, A. Sedlmeier, T. Gabor, and C. Linnhoff-Popien, "Nash Equilibria in Multi-Agent Swarms," in 12th International Conference on Agents and Artificial Intelligence (ICAART 2020), 2020.
    [BibTeX]
    @InProceedings{hahn2020nash,
    author = {Carsten Hahn and Thomy Phan and Sebastian Feld and Christoph Roch and Fabian Ritz and Andreas Sedlmeier and Thomas Gabor and Claudia Linnhoff-Popien},
    title = {Nash Equilibria in Multi-Agent Swarms},
    booktitle = {12th International Conference on Agents and Artificial Intelligence (ICAART 2020)},
    year = {2020},
    owner = {chahn},
    }
  • A. Sedlmeier, T. Gabor, T. Phan, and C. L. Lenz Belzner, "Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning," in 12th International Conference on Agents and Artificial Intelligence (ICAART 2020), 2020.
    [BibTeX]
    @InProceedings{sedlmaier2020UBOOD,
    author = {Andreas Sedlmeier and Thomas Gabor and Thomy Phan and Lenz Belzner, Claudia Linnhoff-Popien},
    title = {Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning},
    booktitle = {12th International Conference on Agents and Artificial Intelligence (ICAART 2020)},
    year = {2020},
    owner = {chahn},
    }

2019

  • M. Friedrich, F. Guimera Cuevas, A. Sedlmeier, and A. Ebert, "Evolutionary Generation of Primitive-Based Mesh Abstractions," in Proceedings of the 27th International Conference on Computer Graphics, Visualization and Computer Vision (WSCG), 2019.
    [BibTeX]
    @InProceedings{friedrich2019generation-mesh,
    author = {Friedrich, Markus and Guimera Cuevas, Felip and Sedlmeier, Andreas and Ebert, André},
    title = {Evolutionary Generation of Primitive-Based Mesh Abstractions},
    booktitle = {Proceedings of the 27th International Conference on Computer Graphics, Visualization and Computer Vision (WSCG)},
    year = {2019},
    owner = {mfriedrich},
    }
  • T. Gabor, A. Sedlmeier, M. Kiermeier, T. Phan, M. Henrich, M. Picklmair, B. Kempter, C. Klein, H. Sauer, R. Schmid, and J. Wieghardt, "Scenario Co-Evolution for Reinforcement Learning on a GridWorld Smart Factory Domain," in Genetic and Evolutionary Computation Conference (GECCO), 2019.
    [BibTeX]
    @InProceedings{Gabor2019a,
    author = {Thomas Gabor and Andreas Sedlmeier and Marie Kiermeier and Thomy Phan and Marcel Henrich and Monika Picklmair and Bernhard Kempter and Cornel Klein and Horst Sauer and Reiner Schmid and Jan Wieghardt},
    title = {Scenario Co-Evolution for Reinforcement Learning on a GridWorld Smart Factory Domain},
    booktitle = {Genetic and Evolutionary Computation Conference (GECCO)},
    year = {2019},
    publisher = {ACM},
    __markedentry = {[gruttaue:6]},
    owner = {tgabor},
    }
  • S. Feld, A. Sedlmeier, M. Friedrich, J. Franz, and L. Belzner, "Bayesian Surprise in Indoor Environments," in 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’19), 2019.
    [BibTeX]
    @InProceedings{feld2019bayesian,
    author = {Feld, Sebastian and Sedlmeier, Andreas and Friedrich, Markus and Franz, Jan and Belzner, Lenz},
    title = {Bayesian Surprise in Indoor Environments},
    booktitle = {27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’19)},
    year = {2019},
    organization = {ACM}
    }
  • A. Sedlmeier, T. Gabor, T. Phan, and L. Belzner, "Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning," in 1st International Symposium on Applied Artificial Intelligence (ISAAI'19), 2019.
    [BibTeX]
    @InProceedings{sedlmeier2019uncertainty,
    author = {Andreas Sedlmeier and Thomas Gabor and Thomy Phan and Lenz Belzner},
    title = {Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning},
    booktitle = {1st International Symposium on Applied Artificial Intelligence (ISAAI'19)},
    year = {2019},
    owner = {asedlmeier},
    }

2018

  • A. Sedlmeier and S. Feld, "Discovering and Learning Recurring Structures in Building Floor Plans," in LBS 2018: 14th International Conference on Location Based Services, 2018, pp. 151-170.
    [BibTeX]
    @InProceedings{sedlmeier2018discovering,
    author = {Sedlmeier, Andreas and Feld, Sebastian},
    title = {Discovering and Learning Recurring Structures in Building Floor Plans},
    booktitle = {LBS 2018: 14th International Conference on Location Based Services},
    year = {2018},
    pages = {151--170},
    publisher = {Springer},
    owner = {asedlmeier},
    }
  • T. Gabor, M. Kiermeier, A. Sedlmeier, B. Kempter, C. Klein, H. Sauer, R. Schmid, and J. Wieghardt, "Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm," in International Symposium on Leveraging Applications of Formal Methods (ISoLA), 2018.
    [BibTeX]
    @inproceedings{gabor2018adapting,
    title={Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm},
    author={Thomas Gabor and Marie Kiermeier and Andreas Sedlmeier and Bernhard Kempter and Cornel Klein and Horst Sauer and Reiner Schmid and Jan Wieghardt},
    booktitle={International Symposium on Leveraging Applications of Formal Methods (ISoLA)},
    year={2018}
    }
  • S. Feld, S. Illium, A. Sedlmeier, and L. Belzner, "Trajectory annotation using sequences of spatial perception," in 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’18), 2018, pp. 1-10. doi:https://doi.org/10.1145/3274895.3274968
    [BibTeX]
    @InProceedings{feld2018trajectory,
    author = {Feld, Sebastian and Illium, Steffen and Sedlmeier, Andreas and Belzner, Lenz},
    title = {Trajectory annotation using sequences of spatial perception},
    booktitle = {26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’18)},
    year = {2018},
    pages = {1--10},
    organization = {ACM},
    doi = {https://doi.org/10.1145/3274895.3274968},
    }
  • A. Sedlmeier and S. Feld, "Learning indoor space perception," Journal of Location Based Services, pp. 1-36, 2018. doi:10.1080/17489725.2018.1539255
    [BibTeX] [Download PDF]
    @Article{sedlmeier2018learning,
    author = {Andreas Sedlmeier and Sebastian Feld},
    title = {Learning indoor space perception},
    journal = {Journal of Location Based Services},
    year = {2018},
    volume = {0},
    number = {0},
    pages = {1-36},
    doi = {10.1080/17489725.2018.1539255},
    owner = {asedlmeier},
    url = {https://doi.org/10.1080/17489725.2018.1539255},
    }

2011

  • M. Dürr, M. Duchon, K. Wiesner, and A. Sedlmeier, "Distributed Group and Rights Management for Mobile Ad Hoc Networks," in 4th Joint IFIP Wireless and Mobile Networking Conference (WMNC 2011), 2011, pp. 1-8. doi:10.1109/WMNC.2011.6097258
    [BibTeX]
    @InProceedings{durr2011b,
    Title = {Distributed Group and Rights Management for Mobile Ad Hoc Networks},
    Author = {Michael Dürr and Markus Duchon and Kevin Wiesner and Andreas Sedlmeier},
    Booktitle = {4th Joint IFIP Wireless and Mobile Networking Conference (WMNC 2011)},
    Year = {2011},
    Pages = {1-8},
    Publisher = {IEEE},
    Doi = {10.1109/WMNC.2011.6097258}
    }

Preprints / Postprints