Effective learning techniques for military applications using the Personalized Assistant that Learns (PAL) enhanced Web-Based Temporal Analysis System (WebTAS)
Peter M. LaMonica, Roger J. Dziegiel, Raymond A. Liuzzi, James Hepler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
The Personalized Assistant that Learns (PAL) Program is a Defense Advanced Research Projects Agency (DARPA) research effort that is advancing technologies in the area of cognitive learning by developing cognitive assistants to support military users, such as commanders and decision makers. The Air Force Research Laboratory's (AFRL) Information Directorate leveraged several core PAL components and applied them to the Web-Based Temporal Analysis System (WebTAS) so that users of this system can have automated features, such as task learning, intelligent clustering, and entity extraction. WebTAS is a modular software toolset that supports fusion of large amounts of disparate data sets, visualization, project organization and management, pattern analysis and activity prediction, and includes various presentation aids. WebTAS is predominantly used by analysts within the intelligence community and with the addition of these automated features, many transition opportunities exist for this integrated technology. Further, AFRL completed an extensive test and evaluation of this integrated software to determine its effectiveness for military applications in terms of timeliness and situation awareness, and these findings and conclusions, as well as future work, will be presented in this report.