On the Strategies of Defending a Target against Multiple Intruders
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-1861.vid This paper investigates a counter-UAS application where a defender is responsible for guarding a target area by intercepting multiple intruding drones. Under the assumption that the intruders also attempt to achieve optimal performance, the intruding strategy is solved together with the defending strategy, using a geometric concept called the dominant region. The defending strategy is inspired by a modified RRT* algorithm that minimizes the defender's cost function, but achieves the goal in a more efficient way. Two categories of strategies are explored, one with location information only, while the other includes velocity information as well. Due to the superiority in number, the intruders are able to take more advantages of the velocity information. For both the defender and the intruders, the improvement in performance is closely related to how much of the opponent's future behaviors can be predicted correctly.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it