A framework for predicting the mission-specific performance of autonomous unmanned systems
Bibliographic record
Abstract
While many methodologies have been proposed for calculating a quantitative level of autonomy for intelligent Unmanned Systems (UMS), no one definitive measure of autonomy or autonomous performance has been validated and adopted by the UMS community. Particularly for military applications, a simple performance metric that is based on the UMSs mission profile and is comparable between UMS systems is critical. This metric would not only help define the features a UMS needs to successfully perform its mission, both in terms of hardware and software, but also enable the use of UMS for a broader range of applications at an increased level of autonomy. This paper presents the development of a new methodology for calculating a single-number performance metric for autonomous UMS, and this metric is called the Mission Performance Potential (MPP). Rather than a retroactive measure of UMS performance and autonomy level for one iteration of a given scenario, the MPP separates autonomy level and mission performance to provide a predictive measure of a UMS's expected performance for a mission set and level of autonomy. As an example application, the MPP is calculated for an Unmanned Aerial Vehicle (UAV) performing a target tracking mission, and this MPP value is compared to the results of field-testing with this system.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".