Lehrstuhl für Organic Computing

  1. University
  2. Faculties
  3. Faculty of Applied Computer Science
  4. Institute of Computer Science
  5. Chairs and Professors
  6. Organic Computing Group
  7. Team
  8. Michael Heider

Michael Heider

Research Assistant
Lehrstuhl für Organic Computing
Phone: +49 821 598 69261
Email: michael.heider@informatik.uni-augsburg.de
Room: 1031 (W)
Address: Am Technologiezentrum 8, 86159 Augsburg

Research foci

My main research project aims to automate parameter optimization of industrial machinery by including existing expert knowledge into advanced machine learning algorithms, such as Learning Classifier Systems, a family of evolutionary learning methods. Therefore, I am developing a new way of creating LCS models (that can be thought of as generalisations of decision trees) with the Supervised Rule-based Learning System (SupRB). In our case study, we try to implement our concepts in the context of plastic extrusion manufacturing. An important role in finding optimal parameter settings plays predicting the quality, that results from applying a given setting. However, operators, supervisors, process engineers and management do all request that these predictive models offer explainability, interpretability and transparency (ergo, be somewhat understandable to a human user).

 

  • evolutionary rule-based learning (supervised and reinforcement learning)
  • unsupervised deep learning for feature extraction (e.g. Autoencoders)
  • explainable AI (XAI)
  • socio-technical assistance systems
  • extrusion-based manufacturing
  • 3D printing / additive manufacturing

Publications

Heider, Michael
2022 | 2021 | 2020 | 2019 | 2016

2022

Michael Heider, David Pätzel and Alexander R. M. Wagner. 2022. An overview of LCS research from 2021 to 2022. Proceedings of the Genetic and Evolutionary Computation Conference Companion 2086-2094. DOI: 10.1145/3520304.3533985
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Helena Stegherr, Michael Heider and Jörg Hähner. 2022. Classifying metaheuristics: towards a unified multi-level classification system. Natural Computing 21, 155-171. DOI: 10.1007/s11047-020-09824-0
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Jonathan Wurth, Michael Heider, Helena Stegherr, Roman Sraj and Jörg Hähner. 2022. Comparing different metaheuristics for model selection in a supervised learning classifier system. Proceedings of the Genetic and Evolutionary Computation Conference Companion 316-319. DOI: 10.1145/3520304.3529015
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Richard Nordsieck, Michael Heider, Alwin Hoffmann and Jörg Hähner. 2022. Reliability-based aggregation of heterogeneous knowledge to assist operators in manufacturing. 2022 IEEE 16th International Conference on Semantic Computing (ICSC) 131-138. DOI: 10.1109/icsc52841.2022.00027
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Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj and Jörg Hähner. 2022. Separating rule discovery and global solution composition in a learning classifier system. Proceedings of the Genetic and Evolutionary Computation Conference Companion 248-251. DOI: 10.1145/3520304.3529014
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2021

David Pätzel, Michael Heider and Alexander R. M. Wagner. 2021. An overview of LCS research from 2020 to 2021. In Francisco Chicano and Krzysztof Krawiec (Ed.). Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lille, France, July 10 - 14, 2021. ACM, New York, NY, 1648-1656. DOI: 10.1145/3449726.3463173
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Andreas Wiedholz, Michael Heider, Richard Nordsieck, Andreas Angerer, Simon Dietrich and Jörg Hähner. 2021. CAD-based grasp and motion planning for process automation in fused deposition modelling. In Oleg Gusikhin, Henk Nijmeijer and Kurosh Madani (Ed.). Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics, July 6-8, 2021. SciTePress, Setúbal, 450-458. DOI: 10.5220/0010571204500458
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Helena Stegherr, Michael Heider, Leopold Luley and Jörg Hähner. 2021. Design of large-scale metaheuristic component studies. In Francisco Chicano and Krzysztof Krawiec (Ed.). Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lille, France, July 10 - 14, 2021. ACM, New York, NY, 1217-1226. DOI: 10.1145/3449726.3463168
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Richard Nordsieck, Michael Heider, Anton Winschel and Jörg Hähner. 2021. Knowledge extraction via decentralized knowledge graph aggregation. In Dick Bulterman, Atsushi Kitazawa, David Ostrowski, Phillip Sheu and Jeffrey Tsai (Ed.). 2021 IEEE 15th International Conference on Semantic Computing (ICSC), 27-29 January 2021, Laguna Hills, CA, USA. IEEE, Piscataway, NJ, 92-99. DOI: 10.1109/icsc50631.2021.00024
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Michael Heider, Richard Nordsieck and Jörg Hähner. 2021. Learning classifier systems for self-explaining socio-technical-systems. In Anthony Stein, Sven Tomforde, Jean Botev and Peter Lewis (Ed.). LIFELIKE 2020 & LIFELIKE 2021, Lifelike Computing Systems Workshop 2020 and 2021: Joint Proceedings of the LIFELIKE 2020 - 8th Edition in the Evolution of the Workshop Series of Autonomously Learning and Optimizing Systems (SAOS) co-located with 2020 Conference on Artificial Life (ALIFE 2020), Online, July 16th, 2020, and the LIFELIKE 2021 - 9th Edition in the Evolution of the Workshop Series of Autonomously Learning and Optimizing Systems (SAOS) co-located with 2021 Conference on Artificial Life (ALIFE 2021), Online, July 19th, 2021. CEUR-WS (CEUR Workshop Proceedings ; Vol-3007)
PDF | BibTeX | RIS | URL

2020

Richard Nordsieck, Michael Heider, Andreas Angerer and Jörg Hähner. 2020. Evaluating the effect of user-given guiding attention on the learning process. In Esam El-Araby, Sven Tomforde, Timothy Wood, Pradeep Kumar, Claudia Raibulet, Ioan Petri, Gabriele Valentini, Phyllis Nelson and Barry Porter (Ed.). 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), 17-21 August 2020, Washington, DC, USA. IEEE, Piscataway, NJ, 215-221. DOI: 10.1109/acsos49614.2020.00044
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Michael Heider, David Pätzel and Jörg Hähner. 2020. SupRB: a supervised rule-based learning system for continuous problems. preprint.
PDF | BibTeX | RIS | URL

Michael Heider, David Pätzel and Jörg Hähner. 2020. Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations. In Carlos Artemio Coello Coello (Ed.). Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO '20), Cancún, Mexico, July 2020. ACM, New York, NY, 127-128. DOI: 10.1145/3377929.3389963
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2019

Michael Heider. 2019. Increasing reliability in FDM manufacturing. In C. Draude, M. Lange and B. Sick (Ed.). Informatik 2019: 50 Jahre Gesellschaft für Informatik - Informatik für Gesellschaft (Workshop-Beiträge). Gesellschaft für Informatik e.V., Bonn, 483-491. DOI: 10.18420/inf2019_ws52
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Richard Nordsieck, Michael Heider, Andreas Angerer and Jörg Hähner. 2019. Towards automated parameter optimisation of machinery by persisting expert knowledge. In Oleg Gusikhin, Kurosh Madani and Janan Zaytoon (Ed.). Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO), July 29-31, 2019, Prague, Czech Republic. SciTePress, Setúbal, 406-413. DOI: 10.5220/0007953204060413
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2016

Sebastian von Mammen, Heiko Hamann and Michael Heider. 2016. Robot gardens: an augmented reality prototype for plant-robot biohybrid systems. In Dieter Kranzlmüller and Gudrun Klinker (Ed.). Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology, VRST '16, Munich, Germany, November 2016. ACM, New York, NY, 139-142. DOI: 10.1145/2993369.2993400
BibTeX | RIS | DOI

Curriculum/Vitae

since 2019 Research Assistant with the chair for Organic Computing
2015–2018 Master programme Computer Science and Information-oriented Business Management at the University of Augsburg
2012–2016 Bachelor programme Computer Science at the University of Augsburg

Courses / teaching

(applied filters: semester: current | institute: Organic Computing | lecturers: Michael Heider | course types: all)
name semester type
Seminar Organic Computing (Bachelor) winter semester 2022/23 Seminar
Seminar Organic Computing (Master) winter semester 2022/23 Seminar
Studentische Arbeiten am Lehrstuhl Organic Computing winter semester 2022/23 sonstige

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