Michael Heider

Research Assistant
Lehrstuhl für Organic Computing
Phone: +49 821 598 69261
Email:
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

2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2016

2024

Henning Cui, Michael Heider and Jörg Hähner. 2024. Positional bias does not influence Cartesian Genetic Programming with crossover. DOI: 10.1007/978-3-031-70055-2_10
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2023

Markus Görlich-Bucher, Michael Heider, Tobias Ciemala and Jörg Hähner. 2023. A decision-theoretic approach for prioritizing maintenance activities in organic computing systems. DOI: 10.1007/978-3-031-42785-5_3
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Helena Stegherr, Leopold Luley, Jonathan Wurth, Michael Heider and Jörg Hähner. 2023. A framework for modular construction and evaluation of metaheuristics.
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Michael Heider, David Pätzel, Helena Stegherr and Jörg Hähner. 2023. A metaheuristic perspective on learning classifier systems. DOI: 10.1007/978-981-19-3888-7_3
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Michael Heider, Helena Stegherr, Richard Nordsieck and Jörg Hähner. 2023. Assessing model requirements for explainable AI: a template and exemplary case study. DOI: 10.1162/artl_a_00414
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Helena Stegherr, Michael Heider and Jörg Hähner. 2023. Assisting convergence behaviour characterisation with unsupervised clustering. DOI: 10.5220/0012202100003595
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Michael Heider, Helena Stegherr, David Pätzel, Roman Sraj, Jonathan Wurth, Benedikt Volger and Jörg Hähner. 2023. Discovering rules for rule-based machine learning with the help of novelty search. DOI: 10.1007/s42979-023-02198-x
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Jonathan Wurth, Helena Stegherr, Michael Heider, Leopold Luley and Jörg Hähner. 2023. Fast, flexible, and fearless: a rust framework for the modular construction of metaheuristics. DOI: 10.1145/3583133.3596335
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Neele Kemper, Michael Heider, Dirk Pietruschka and Jörg Hähner. in press. Forecasting of residential unit's heat demands: a comparison of machine learning techniques in a real-world case study. DOI: 10.1007/s12667-023-00579-y
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Lukas Meitz, Michael Heider, Thorsten Schöler and Jörg Hähner. 2023. On data-preprocessing for effective predictive maintenance on multi-purpose machines. DOI: 10.5220/0012146700003541
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Markus Görlich-Bucher, Michael Heider and Jörg Hähner. 2023. Predicting physical disturbances in organic computing systems using automated machine learning. DOI: 10.1007/978-3-031-42785-5_4
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Michael Heider, Helena Stegherr, Roman Sraj, David Pätzel, Jonathan Wurth and Jörg Hähner. 2023. SupRB in the context of rule-based machine learning methods: a comparative study. DOI: 10.1016/j.asoc.2023.110706
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David Pätzel, Michael Heider and Jörg Hähner. 2023. Towards principled synthetic benchmarks for explainable rule set learning algorithms. DOI: 10.1145/3583133.3596416
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Tobias Wittmeir, Michael Heider, André Schweiger, Michaela Krä, Jörg Hähner, Johannes Schilp and Joachim Berlak. 2023. Towards robustness of production planning and control against supply chain disruptions. DOI: 10.15488/13425
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Henning Cui, Andreas Margraf, Michael Heider and Jörg Hähner. 2023. Towards understanding crossover for Cartesian Genetic Programming. DOI: 10.5220/0012231400003595
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2022

Richard Nordsieck, Michael Heider, Anton Hummel and Jörg Hähner. 2022. A closer look at sum-based embeddings for knowledge graphs containing procedural knowledge.
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Michael Heider, David Pätzel and Alexander R. M. Wagner. 2022. An overview of LCS research from 2021 to 2022. DOI: 10.1145/3520304.3533985
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Michael Heider, Helena Stegherr, David Pätzel, Roman Sraj, Jonathan Wurth, Benedikt Volger and Jörg Hähner. 2022. Approaches for rule discovery in a learning classifier system. DOI: 10.5220/0011542000003332
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Helena Stegherr, Michael Heider and Jörg Hähner. 2022. Classifying metaheuristics: towards a unified multi-level classification system. 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. DOI: 10.1145/3520304.3529015
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Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj and Jörg Hähner. 2022. Investigating the impact of independent rule fitnesses in a learning classifier system. DOI: 10.1007/978-3-031-21094-5_11
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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. 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. DOI: 10.1145/3520304.3529014
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Richard Nordsieck, Michael Heider, Anton Hummel, Alwin Hoffmann and Jörg Hähner. 2022. Towards models of conceptual and procedural operator knowledge.
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2021

David Pätzel, Michael Heider and Alexander R. M. Wagner. 2021. An overview of LCS research from 2020 to 2021. 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. 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. 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. 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.
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2020

Richard Nordsieck, Michael Heider, Andreas Angerer and Jörg Hähner. 2020. Evaluating the effect of user-given guiding attention on the learning process. 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.
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Michael Heider, David Pätzel and Jörg Hähner. 2020. Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations. DOI: 10.1145/3377929.3389963
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2019

Michael Heider. 2019. Increasing reliability in FDM manufacturing. 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. 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. DOI: 10.1145/2993369.2993400
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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 2024/25 Seminar
Studentische Arbeiten am Lehrstuhl Organic Computing winter semester 2024/25 sonstige
Seminar Organic Computing (Master) winter semester 2024/25 Seminar

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