79 citations · 370 across the 33 of their papers we have counts for
6 papers · 1 filter
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
Haoyu Ren, Darko Anicic, Thomas Runkler
Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in rea…
Combining Sub-Symbolic and Symbolic Methods for Explainability
Anna Himmelhuber, Stephan Grimm, Sonja Zillner +3
Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide…
Ontology-Based Skill Description Learning for Flexible Production Systems
Anna Himmelhuber, Stephan Grimm, Thomas Runkler +1
The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing ma…
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming
Daniel Hein, Steffen Udluft, Thomas A. Runkler
Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers…
Interpretable Policies for Reinforcement Learning by Genetic Programming
Daniel Hein, Steffen Udluft, Thomas A. Runkler
The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deplo…
A Benchmark Environment Motivated by Industrial Control Problems
Daniel Hein, Stefan Depeweg, Michel Tokic +4
In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are…