12 citations · 18 across the 9 of their papers we have counts for
9 papers
Information-seeking failures of large language models in agentic clinical reasoning
Krischan Braitsch, Laura K. Schmalbrock, Theresa Weltermann +11
Large language models achieve high scores on medical knowledge assessments, yet clinical reasoning requires actively deciding what to investigate under uncertainty. We developed an…
Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
Helena Stegherr, Michael Heider, Nils Meyer +7
Evolutionary computation offers a variety of tools to solve complex real-world optimization problems. However, research often focuses on smaller, simplified problems and optimizati…
Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study
Sergej Krasnikov, Lukas Meitz, Samineh Bagheri +3
Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used benchmark datasets, primarily due to heterogeneous, multi-…
Pareto-Optimal Anytime Algorithms via Bayesian Racing
Jonathan Wurth, Helena Stegherr, Neele Kemper +2
Selecting an optimization algorithm requires comparing candidates across problem instances, but the computational budget for deployment is often unknown at benchmarking time. Curre…
PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing
Richard Nordsieck, André Schweizer, Michael Heider +1
Procedural knowledge describes how to accomplish tasks and mitigate problems. Such knowledge is commonly held by domain experts, e.g. operators in manufacturing who adjust paramete…
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
Michael Heider, Helena Stegherr, Jonathan Wurth +2
Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based…