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Finding Action Dependencies Using the Crowd
| Content Provider | CiteSeerX |
|---|---|
| Author | Lasecki, Walter S. Weingard, Leon Ferguson, George Bigham, Jeffrey P. |
| Abstract | Training intelligent systems is a time-consuming and costly process that often limits real-world applications. Prior work has attempted to compensate for this challenge by generat-ing sets of labeled training data for machine learning algo-rithms using affordable human contributors. In this paper, we present ARchitect, a system that uses the crowd to ex-tract context-dependent relational structure. We focus on activity recognition because of its broad applicability, high level of variation, and difficulty of training systems a priori. We demonstrate that using our approach, the crowd can accurately and consistently identify relationships between actions even over sessions containing different workers and varied executions of an activity. This results in the abil-ity to identify multiple valid execution paths from a single observation, suggesting that one-off learning can be facili-tated by using the crowd as an on-demand source of human intelligence in the knowledge acquisition process. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
