Defending a Target Area With a Slower Defender
Why this work is in the frame
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Bibliographic record
Abstract
The target defense game is an abstraction of the counter-UAV mission, where a defender intends to intercept an invading drone before it enters a target area. While most studies on target defense games assume the defender travels faster, defending a target area with a slower defender is a less studied yet challenging problem, because capture cannot be guaranteed. This letter identifies two special cases where the defender has a chance to win, where the game region is bounded and where the target area is small. In the former case, the defender traps the invader at the corner. In the latter case, the defender delays the entering permanently by rotating around the target area at a sufficiently large angular speed. In both games, the optimal trajectory has a two-stage structure. Exploiting this feature, a novel method is proposed to solve for the barrier, which gives guidelines on how to deploy the defenders to ensure the target area been protected.
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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) | 0.001 | 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