The “prince” technique: Fitts' law and selection using area cursors
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Haptic output in multimodal user interfaces
Proceedings of the 2nd international conference on Intelligent user interfaces
Making computers easier for older adults to use: area cursors and sticky icons
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Acquisition of expanding targets
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Semantic pointing: improving target acquisition with control-display ratio adaptation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Object pointing: a complement to bitmap pointing in GUIs
GI '04 Proceedings of the 2004 Graphics Interface Conference
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The bubble cursor: enhancing target acquisition by dynamic resizing of the cursor's activation area
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
"Beating" Fitts' law: virtual enhancements for pointing facilitation
International Journal of Human-Computer Studies - Special issue: Fitts law 50 years later: Applications and contributions from human-computer interaction
Predictive interaction using the delphian desktop
Proceedings of the 18th annual ACM symposium on User interface software and technology
Fitts' law and expanding targets: Experimental studies and designs for user interfaces
ACM Transactions on Computer-Human Interaction (TOCHI)
ICML '06 Proceedings of the 23rd international conference on Machine learning
An evaluation of sticky and force enhanced targets in multi target situations
Proceedings of the 4th Nordic conference on Human-computer interaction: changing roles
Endpoint prediction using motion kinematics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 20th annual ACM symposium on User interface software and technology
Ninja cursors: using multiple cursors to assist target acquisition on large screens
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
DynaSpot: speed-dependent area cursor
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research
User target intention recognition from cursor position using kalman filter
UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: design methods, tools, and interaction techniques for eInclusion - Volume Part I
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Numerous interaction techniques have been developed that make "virtual" pointing at targets in graphical user interfaces easier than analogous physical pointing tasks by invoking target-based interface modifications. These pointing facilitation techniques crucially depend on methods for estimating the relevance of potential targets. Unfortunately, many of the simple methods employed to date are inaccurate in common settings with many selectable targets in close proximity. In this paper, we bring recent advances in statistical machine learning to bear on this underlying target relevance estimation problem. By framing past target-driven pointing trajectories as approximate solutions to well-studied control problems, we learn the probabilistic dynamics of pointing trajectories that enable more accurate predictions of intended targets.