Probabilistic Control in Human Computer Interaction
published: Jan. 19, 2010, recorded: December 2009, views: 4250
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Continuous interaction with computers can be treated as a control problem subject to various sources of uncertainty. We present examples of interaction based on multiple noisy sensors (capacitive sensing, location- and bearing sensing and EEG), in domains which rely on inference about user intention, and where the use of particle filters can improve performance. We use the "H-metaphor" for automated, flexibly handover of level of autonomy in control, as a function of the certainty of control actions from the user, in an analogous fashion to 'loosening the reins' when horse-riding. Integration of the inference mechanisms with probabilistic feedback designs can have a significant effect on behaviour, and some examples are presented. (Joint work with John Williamson, Simon Rogers and Steven Strachan).
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !