SBIR-STTR Award

This proposal describes how adaptive training for tactical decision making can be obtained through the use of Virtual Observer/Controllers (VO/C), which shall be developed to mimic human observer/controllers (O/Cs). Our objective in this proposal is to use our patented reflection technology to model VO/Cs that can perform the jobs of a human O/C for an Anti-Submarine Warfare Evaluator (ASWE), thus developing an adaptive training solution for ASW that effectively and efficiently individualizes training for trainees by identifying non-optimal instances of state and/or performance, identifying the root cause(s) of issues, mitigating root cause(s) of issues in an appropriate manner, and summarizing trainee status post-scenario via a comprehensive, targeted AAR. The end result is a training system that helps to enable realistic training. Although our approach shall be general, the proposed effort shall focus on the needs of our HIFAST transition partner, with initial focus on the ASWE. The ASWE is critical to operations and targeted for early transition of training. The proposed Virtual Observer Controllers for Adaptive Training (VOCAT) shall leverage work from past efforts in creating domain specific consoles to support instructors in training. We focus on the capture of expertise from instructors and the design of the VOCAT console.

Keywords:
training, Cognitive Performance, Performance Measurements, Knowledge Capture, Artificial Intelligence, Cognitive Science, Mental Models, Anti-Submarine Warfare

It has long been established that the best way to learn is to have a private tutor who works with you one-on-one, adapting the training to your learning needs. In such settings, one can: ask questions, get feedback on an approach, and suggestions on how to improve. A skilled tutor will identify a students strengths and weaknesses and adapt teaching to emphasize solutions that take advantage of the students individual talents. Private tutors are too costly for all but the most affluent, while experts are, in general, too busy to mentor as they once did. The need for individualized instruction, however, has remained. This is particularly true in complex decision making environments such as Anti-Submarine Warfare where trainees need to be prepared for situations that are highly varied. Discovery Machine Incorporated describes how adaptive training for tactical decision making can be obtained through the use of Virtual Observer/Controllers, which shall be developed to mimic human observer/controllers(O/Cs). Human O/Cs provide a type of apprentice training. These O/Cs watch what the trainees do and inject challenges based on how the trainees have performed. They also adapt the training to make sure that the trainee is getting the most out of their training