5 papers
Towards Machine Learning-based Fish Stock Assessment
Stefan Lüdtke, Maria E. Pierce
The accurate assessment of fish stocks is crucial for sustainable fisheries management. However, existing statistical stock assessment models can have low forecast performance of r…
Leveraging Planning Landmarks for Hybrid Online Goal Recognition
Nils Wilken, Lea Cohausz, Johannes Schaum +3
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios it is imp…
Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition
Nils Wilken, Lea Cohausz, Johannes Schaum +2
An important feature of pervasive, intelligent assistance systems is the ability to dynamically adapt to the current needs of their users. Hence, it is critical for such systems to…
Outlier Explanation via Sum-Product Networks
Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
Outlier explanation is the task of identifying a set of features that distinguish a sample from normal data, which is important for downstream (human) decision-making. Existing met…
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs
Timon Felske, Stefan Lüdtke, Sebastian Bader +1
We study sensor-based human activity recognition in manual work processes like assembly tasks. In such processes, the system states often have a rich structure, involving object pr…