Bibliographic record
Abstract
The training of military crews of armoured vehicles can be enhanced by applying AI-based methods to the training drills. Defence Research and Development Canada used a Human Behaviour Representation approach to create an armoured crew simulation trainer for the Canadian Armed Forces. The Human Behaviour Representation (HBR) approach is a form of rule-based AI that applies a cognitive task analysis to derive a synthetic operator. The cognitive task analysis resulted in a Task Network Model (TNM) for each crew member of the Light Armoured Vehicle (LAV) and for the entire crew. These TNMs were inputted into a discrete event simulator to create a synthetic training environment that combines virtual and human members of the LAV crew. The training platform allows a human member of the team to interact with the synthetic crew through voice production software that was integrated with the synthetic environment.The paper presents the development of the Intelligent Tutoring System module for the LAV crew simulation platform that serves as a human instructor for conducting basic LAV drills. The paper outlines the architecture, functionality, and testing of the module. The work shows how the HBR approach can be used to develop a synthetic coach for training a military crew. The work is a step in developing and testing a general training system for small military teams. The training system will allow to conduct basic crew drills, in which a human crew member will be trained with the synthetic crew members, thus overcoming some of the obstacles that military crew training faces: a logistic difficulty to gather a full crew at the same time and place and a deficiency of qualified instructors. The paper outlines the steps for the follow-up work required to develop a generic AI-based autonomous systems for basic training of small military teams.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".