14 citations · 16 across the 3 of their papers we have counts for
3 papers
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization
Vilde B. Gjærum, Inga Strümke, Ole Andreas Alsos +1
Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence met…
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning
Sindre Benjamin Remman, Inga Strümke, Anastasios M. Lekkas
We investigate the effect of including domain knowledge about a robotic system's causal relations when generating explanations. To this end, we compare two methods from explainable…
Inferring feature importance with uncertainties in high-dimensional data
Pål Vegard Johnsen, Inga Strümke, Signe Riemer-Sørensen +2
Estimating feature importance is a significant aspect of explaining data-based models. Besides explaining the model itself, an equally relevant question is which features are impor…