(since 2016) University of Hamburg, Germany
Although modeling emotion concepts, most of the works on affective computing does not take into consideration the role of affective modulation in decision making. This project uses the previously proposed affective framework to create an intrinsic motivation mechanism, which modulates and can be modulated by contextual information, perception, memory, actions, and tasks.
Publications associated with this project
- Barros, P., Barakova, E., & Wermter, S. (2018). A Deep Neural Model Of Emotion Appraisal. arXiv preprint arXiv:1808.00252
- Churamani, N., Sutherland, A., Barros, P. An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario. Proceedings of the Workshop on Intelligent Assistive Computing, IEEE World Congress on Computational Intelligence (WCCI) 2018.
- Barros, P., Churamani, N., Lakomkin, E., Siqueira, H., Sutherland, A., & Wermter, S. (2018, July). The OMG-Emotion Behavior Dataset. In Neural Networks (IJCNN), 2018 International Joint Conference on (pp. 1408-1414). IEEE.
- Churamani, N., Barros, P., Strahl, E., & Wermter, S. (2018, July). Learning Empathy-Driven Emotion Expressions using Affective Modulations. In Neural Networks (IJCNN), 2018 International Joint Conference on (pp. 1400-1407). IEEE.
- Barros, P., & Wermter, S. (2017, May). A self-organizing model for affective memory. In Neural Networks (IJCNN), 2017 International Joint Conference on (pp. 31-38). IEEE.
- Churamani, N., Kerzel, M., Strahl, E., Barros, P., & Wermter, S. (2017, May). Teaching emotion expressions to a human companion robot using deep neural architectures. In Neural Networks (IJCNN), 2017 International Joint Conference on (pp. 627-634). IEEE.