Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Structuring programs to support intelligent interfaces
Intelligent user interfaces
Decision making in intelligent user interfaces
Proceedings of the 2nd international conference on Intelligent user interfaces
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Mental models of robotic assistants
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Models of attention in computing and communication: from principles to applications
Communications of the ACM
Predicting Future User Actions by Observing Unmodified Applications
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Exploring importance of location and prior knowledge of environment on mobile robot control
International Journal of Human-Computer Studies
An Agent-Based Architecture for an Adaptive Human-Robot Interface
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 5 - Volume 5
Human-robot interaction methodology
Human-robot interaction methodology
Learning and reasoning about interruption
Proceedings of the 5th international conference on Multimodal interfaces
Exploring the design space for adaptive graphical user interfaces
Proceedings of the working conference on Advanced visual interfaces
Direct manipulation interfaces
Human-Computer Interaction
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
Decision Support Systems
Assessment of adaptive human-robot interactions
Knowledge-Based Systems
| Hi-index | 0.00 |
This project addresses some research issues concerning design of intelligent user interfaces for improving human-robot interaction. In some critical applications, users interact with robots via Graphical User Interfaces (GUIs), which usually contain standard components considering a large number of users. Some of these user interface components may be redundant and sometimes confusing for some users depending on their preferences, capabilities, and the context robots are used in. This paper describes an adaptive system that enables a mobile robot to learn its users' preferences and capabilities so that it can offer a dynamic and efficient GUI for each user rather than a standard GUI for all users. The system predicts future actions of the users by generating models based on the users' previous interactions with the robot. The system was implemented and evaluated on a Pioneer 3-AT mobile robot. About 20 participants who were assessed on spatial ability directed the robot in simple spatial navigation tasks to evaluate effectiveness of the adaptive interface. Time to complete the task, the number of steps, and the number of errors were collected. The results showed that although spatial reasoning ability plays an important role in mobile robot navigation, it is less important in the robot control with adaptive interfaces compared to that of the non-adaptive.