Interactive computer systems’ designers emphasize the importance of considering humans, their emotions, and behaviors as first-class entities.Emotions are integral parts of human nature, and ignoring that can lead the interactive systems to failure, low quality, or discomfort.User interfaces (UIs) are increasingly becoming adaptive to users’ various characteristics, intending to improve users’ satisfaction, performance, and decisions.However, the previous approaches proposed for supervising such adaptations are not effectively adopted in real-life problems.This paper proposes the novel approach to adapting UIs to users’ emotions using Model-Free Reinforcement Learning (MFRL).The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ task completion and satisfaction.We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger.We performed experiments with a mobile application we developed that acts as a recommender system in emergency training.By taking contextual input of the users’ basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while arousing target emotions.The research includes literature analysis, surveys, and further adopting an iterative process in implementation and experimentation.The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other possible UI adaptation techniques, i.e., rule-based and sequential adaptation.
|Title of host publication
|UMAP 2023 : Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
|Association for Computing Machinery
|Published - Jun 2023
|31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023 - Limassol, Cyprus
Duration: 26. Jun 2023 → 30. Jun 2023
|31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023
|26/06/2023 → 30/06/2023
|ACM SIGCHI, ACM SIGWEB, NSF, Springer Nature, User Modeling Inc.
- Reinforcement Learning
- User Interface