Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
ACM SIGCHI curricula for human-computer interaction
ACM SIGCHI curricula for human-computer interaction
COACH: a teaching agent that learns
Communications of the ACM
The media equation: how people treat computers, television, and new media like real people and places
Does computer-generated speech manifest personality? an experimental test of similarity-attraction
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web
User Modeling and User-Adapted Interaction
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Modeling Student Knowledge: Cognitive Tutors in High School and College
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
MAUI: a multimodal affective user interface
Proceedings of the tenth ACM international conference on Multimedia
Emotion recognition from physiological signals using wireless sensors for presence technologies
Cognition, Technology and Work
Using noninvasive wearable computers to recognize human emotions from physiological signals
EURASIP Journal on Applied Signal Processing
Affectively Intelligent User Interfaces for Enhanced E-Learning Applications
HCD 09 Proceedings of the 1st International Conference on Human Centered Design: Held as Part of HCI International 2009
A step towards adaptive multimodal virtual social interaction platform for children with autism
UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: user and context diversity - Volume 2
Sentiment analysis in Facebook and its application to e-learning
Computers in Human Behavior
Hi-index | 0.00 |
In this paper we describe the User Modeling phase of our general research approach: developing Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction. Natural communication is established by recognizing users' affective states (i.e., emotions experienced by the users) and responding to those emotions by adapting to the current situation via an affective user model. Adaptation of the interface was designed to provide multi-modal feedback to the users about their current affective state and to respond to users' negative emotional states in order to compensate for the possible negative impacts of those emotions. Bayesian Belief Networks formalization was employed to develop the User Model to enable the intelligent system to appropriately adapt to the current context and situation by considering user-dependent factors, such as: personality traits and preferences.