Trainee Evaluation Through After-action Review by Comparison

Abstract

We describe an investigation into how to automate after-action review (AAR) to provide non-trivial, individual feedback to trainees in a military training context. On a high-level basis, our approach is to monitor the actions of the trainee(s) and compare them with those of software agents (called expert agents) whose behavior represents that of an expert-level performer. By identifying and logging discrepancies between the trainee and the expert agent, a measure of valuable feedback can be given to the trainee to whom the expert agent was assigned to ‘shadow’. The comparisons are made in two dimensions concurrently: the physical dimension and the tactical dimension. In a physical comparison, the trainee is compared with the physical location of the expert agent. In the tactical comparison, the context of the agent is compared with that of the trainee. If the latter comparison agrees, then it can be said that the trainee is employing the same tactics as the agent. The context of the trainee is un-intrusively inferred through a novel process called context agents. A prototype is built and tested with data from an instrumented live exercise. Results indicate that the procedure has significant promise to provide much-needed automation in AAR.