Artificial Neural Networks excel at identifying individual components in an image.However, out-of-the-box, they do not manage to correctly integrate and interpret these components as a whole.One way to alleviate this weakness is to expand the network with explicit knowledge and a separate reasoning component.In this paper, we evaluate an approach to this end, applied to the solving of the popular board game Rummikub.We demonstrate that, for this particular example, the added background knowledge is equally valuable as two-thirds of the data set, and allows to bring down the training time to half the original time.* This research received funding from the Flemish

We introduce FindingEmo, a new image dataset containing annotations for 25k images, specifically tailored to Emotion Recognition. Contrary to existing datasets, it focuses on complex scenes depicting multiple people in various naturalistic, social settings, with images being annotated as a whole, thereby going beyond the traditional focus on faces or single individuals. Annotated dimensions include Valence, Arousal and Emotion label, with annotations gathered using Prolific. Together with the annotations, we release the list of URLs pointing to the original images, as well as all associated source code.