The virtual infantry soldier: integrating physical and cognitive digital human simulation in a street battle scenario
Bibliographic record
Abstract
Simulation has become a powerful method for military research and combat training due to its intuitive visualization, repeatability, and security in contrast to real-world training. Previous studies often divided cognitive and physical factors into isolated models using separated platforms. Ideally, both cognitive and physical aspects of a virtual soldier should be modeled on the same platform. We demonstrated an integrated modeling that combines cognitive models with physical human models. A simple task was used, requiring the virtual soldier to navigate in a virtual city, avoid enemies, and reach the destination asap. The Queueing Network-Adaptive Control of Thought Rational cognitive model helps the virtual soldier make choices after encountering enemies. Based on the information collected, the soldier will choose different strategies. Two general-purpose methods from the cognitive modeling and digital human modeling were combined. The results were able to capture the behavioral states as planned and visualize the movement of the virtual soldier, who was able to complete the task as expected. The results demonstrated the feasibility of integrated models combining cognitive and physical aspects of human performance in the application of virtual soldiers. Future studies could further compare the results of model output with human empirical data to validate the modeling capabilities.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".