PerFECT: An Automated Framework for Training on the Fly



Currently available cognitive training systems can highly benefit from more adaptable and encapsulated frameworks that include better performance assessment methods, robust feedback mechanisms and automated mechanisms that reduce the manual intervention and curriculum management required during training sessions. In short, there is an ardent need for an automated human in the loop training system that can effectively train cognitive skills required for military operations. An automated training system would be extremely beneficial if it can be easily coupled with a synthetic learning environment to function autonomously is an entirely data driven manner. Such a system would enable rapid deployment of key training scenarios, skills and tactics to war fighters and help them maintain a superior level of competence in the battlefield. An automated framework for training on the fly also known as performance feedback engine for conflict training (PerFECT) which includes key components for simulating training scenarios, measuring trainee’s performance, providing relevant feedback and dynamic curriculum management is discussed in this chapter. First, the training system comprises of custom plug-in interface that allows components of the training framework to readily interface with a simulated virtual learning environment. Second, it has a “Performance Evaluator” that enables automated, real-time and objective evaluation of a trainee’s performance grounded within an objective framework known as time window and enables run-time evaluation of performance skills based on a skills matrix. Third, PerFECT has a “Feedback System” that can provide contextual and immediate feedback to trainees based on process measures. Finally, PerFECT includes a “Curriculum Manager” that dynamically selects appropriate training scenario from a template library with varying levels of complexity. The selection algorithm for training scenario is based on the trainee’s historical performance scores and complexity of the earlier scenarios. We also present the initial findings from a pilot study which helps illustrate the capabilities of the framework and conclude with future directions in this area of research.


Virtual Environment Training System Automate Speech Recognition System Training Scenario Performance Evaluator 



This work supported by DARPA/IPTO under contract# HR0011-09-C-0102. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the DARPA/IPTO. We would like to thank Amy Vanderbilt (DARPA-IPTO), David Montgomery (DARPA-IPTO) and Joseph Traugott (US RDECOM-STTC) for their guidance and mentoring on this project. We would also like to thank Tim Stone (Omega Training, Cubic Corporation) for his support and shaping our understanding of the training provided to the Fire Teams. We would also like to thank members for CATT Lab (University of Maryland) including Michael Pack, Walter Lucman, Michael VanDaniker and Michael Couture for helping us understand the capabilities of the OLIVE Virtual Environment. Second Life is a registered trademark of Linden Research, Inc. OLIVE is a trademark of Forterra Systems Inc (now part of SAIC). All other trademarks used herein are the property of their respective owners.

The views, opinions, and/or findings contained in this article/presentation are those of the author/presenter and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Hari Thiruvengada
    • 1
  • Anand Tharanathan
    • 1
  • Paul Derby
    • 1
  1. 1.Honeywell ACS LabsMinneapolisUSA

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