Specialization of perceptual processes
Specialization of perceptual processes
Building embedded conceptual parsers
Building embedded conceptual parsers
Multimodal user interfaces in the Open Agent Architecture
Proceedings of the 2nd international conference on Intelligent user interfaces
Ten myths of multimodal interaction
Communications of the ACM
Improving human computer interaction in a classroom environment using computer vision
Proceedings of the 5th international conference on Intelligent user interfaces
Maintaining knowledge about temporal intervals
Communications of the ACM
Autonomous Agents and Multi-Agent Systems
Intelligent Control of Life Support for Space Missions
IEEE Intelligent Systems
The Dynamic Predictive Memory Architecture: Integrating Language with Task Execution
INTSYS '98 Proceedings of the IEEE International Joint Symposia on Intelligence and Systems
Unification-based multimodal parsing
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Finite-state multimodal parsing and understanding
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
An architecture for vision and action
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Provenance and Annotation for Visual Exploration Systems
IEEE Transactions on Visualization and Computer Graphics
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Many intelligent interfaces must recognize patterns of user activity that cross a variety of different input channels. These multimodal interfaces offer significant challenges to both the designer and the software engineer. The designer needs a method of expressing interaction patterns that has the power to capture real use cases and a clear semantics. The software engineer needs a processing model that can identify the described interaction patterns efficiently while maintaining meaningful intermediate state to aid in debugging and system maintenanceIn this paper, we describe an input model, a general recognition model, and a series of important classes of recognition parsers with useful computational characteristics; that is, we can say with some certainty how efficient the recognizers will be, and the kind of patterns the recognizers will accept. Examples illustrate the ability of these recognizers to integrate information from multiple channels across varying time intervals.