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We describe the concept of distributed problem solving and define it as the cooperative solution of problems by a decentralized and loosely coupled collection of problem solvers. This approach to problem solving offers the promise of increased performance and provides a useful medium for exploring and developing new problem-solving techniques. We present a(More)
Freehand sketching is a natural and crucial part of everyday human interaction, yet is almost totally unsupported by current user interfaces. We are working to combine the flexibility and ease of use of paper and pencil with the processing power of a computer, to produce a user interface for design that feels as natural as paper, yet is considerably(More)
— Two forms of cooperation in distributed problem solving are considered: task-sharing and result-sharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial results which are based on somewhat different perspectives on the(More)
central and, in some ways, most familiar concepts in AI, the most fundamental question about it—What is it?—has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important(More)
We have created and tested Tahuti, a dual-view sketch recognition environment for class diagrams in UML. The system is based on a multi-layer recognition framework which recognizes multi-stroke objects by their geometrical properties allowing users the freedom to draw naturally as they would on paper rather than requiring the user to draw the objects in a(More)
Recent progress has shown that learning from hierarchical feature representations leads to improvements in various computer vision tasks. Motivated by the observation that human activity data contains information at various temporal resolutions, we present a hierarchical sequence summarization approach for action recognition that learns multiple layers of(More)