Extending State Transition Diagrams for the Specification of Human-Computer Interaction
IEEE Transactions on Software Engineering - Annals of discrete mathematics, 24
Propositional production systems for dialog description
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Probabilistic state machines: dialog management for inputs with uncertainty
UIST '92 Proceedings of the 5th annual ACM symposium on User interface software and technology
Reusable hierarchical command objects
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A negotiation architecture for fluid documents
Proceedings of the 11th annual ACM symposium on User interface software and technology
A software model and specification language for non-WIMP user interfaces
ACM Transactions on Computer-Human Interaction (TOCHI)
Ten myths of multimodal interaction
Communications of the ACM
Providing integrated toolkit-level support for ambiguity in recognition-based interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Interaction techniques for ambiguity resolution in recognition-based interfaces
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
Proceedings of the 2nd international conference on Tangible and embedded interaction
OctoPocus: a dynamic guide for learning gesture-based command sets
Proceedings of the 21st annual ACM symposium on User interface software and technology
Multimodal Interfaces: A Survey of Principles, Models and Frameworks
Human Machine Interaction
A framework for robust and flexible handling of inputs with uncertainty
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
A user-specific machine learning approach for improving touch accuracy on mobile devices
Proceedings of the 25th annual ACM symposium on User interface software and technology
Machine learning models for uncertain interaction
Adjunct proceedings of the 25th annual ACM symposium on User interface software and technology
The interactive join: recognizing gestures for database queries
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Gesture studio: authoring multi-touch interactions through demonstration and declaration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Focused and casual interactions: allowing users to vary their level of engagement
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Imaginary reality gaming: ball games without a ball
Proceedings of the 26th annual ACM symposium on User interface software and technology
Extending the vocabulary of touch events with ThumbRock
Proceedings of Graphics Interface 2013
Uncertain: a first-order type for uncertain data
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
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Current input handling systems provide effective techniques for modeling, tracking, interpreting, and acting on user input. However, new interaction technologies violate the standard assumption that input is certain. Touch, speech recognition, gestural input, and sensors for context often produce uncertain estimates of user inputs. Current systems tend to remove uncertainty early on. However, information available in the user interface and application can help to resolve uncertainty more appropriately for the end user. This paper presents a set of techniques for tracking the state of interactive objects in the presence of uncertain inputs. These techniques use a Monte Carlo approach to maintain a probabilistically accurate description of the user interface that can be used to make informed choices about actions. Samples are used to approximate the distribution of possible inputs, possible interactor states that result from inputs, and possible actions (callbacks and feedback) interactors may execute. Because each sample is certain, the developer can specify most of the behavior of interactors in a familiar, non-probabilistic fashion. This approach retains all the advantages of maintaining information about uncertainty while minimizing the need for the developer to work in probabilistic terms. We present a working implementation of our framework and illustrate the power of these techniques within a paint program that includes three different kinds of uncertain input.