Abstract
Performance of a cognitive personal assistant, RADAR, consisting of multiple machine learning components, natural language processing, and optimization was examined with a test explicitly developed to measure the impact of integrated machine learning when used by a human user in a real world setting. Three conditions (conventional tools, Radar without learning, and Radar with learning) were evaluated in a large-scale, between-subjects study. The study revealed that integrated machine learning does produce a positive impact on overall performance. This paper also discusses how specific machine learning components contributed to human-system performance.
| Original language | English (US) |
|---|---|
| Pages | 182-188 |
| Number of pages | 7 |
| State | Published - Dec 1 2007 |
| Event | 2007 Performance Metrics for Intelligent Systems Workshop, PerMIS'07 - Gaithersburg, MD, United States Duration: Aug 28 2007 → Aug 30 2007 |
Other
| Other | 2007 Performance Metrics for Intelligent Systems Workshop, PerMIS'07 |
|---|---|
| Country | United States |
| City | Gaithersburg, MD |
| Period | 8/28/07 → 8/30/07 |
Keywords
- Evaluation
- Intelligent systems
- Machine learning
- Mixed-initiative assistants
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
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Steinfeld, A., Bennett, S. R., Cunningham, K., Lahut, M., Quinones, P. A., Wexler, D., Siewiorek, D., Hayes, J., Cohen, P., Fitzgerald, J., Hansson, O., Pool, M., & Drummond, M. (2007). Evaluation of an integrated multi-task machine learning system with humans in the loop. 182-188. Paper presented at 2007 Performance Metrics for Intelligent Systems Workshop, PerMIS'07, Gaithersburg, MD, United States.