Probabilistic Control in Human Computer Interaction
published: Jan. 19, 2010, recorded: December 2009, views: 4248
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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).
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