Optimal Solution of a Target Defense Game with Two defenders and a Faster Intrude
Bibliographic record
Abstract
A target defense game with two defenders and a faster intruder is solved based on the classic differential game theory. In the game, the intruder seeks to enter a circular target area, while the defenders endeavor to capture it outside of the target. Under the faster intruder assumption, the game has two phases, where the optimal trajectories are straight and curved, respectively. In the second phase, a peculiar phenomenon exists where the intruder moves at the edge of one defender’s capture region, yet this defender cannot force capture. Because of this, the terminal states of the game are singular, therefore the standard method of integrating optimal trajectories from terminal states is not applicable. The way to circumvent this singularity is to solve the optimal trajectories of a two-player game between the intruder and the closer defender, and assemble them with the trajectory of the other defender. The key contribution of this paper is the solution of the intruder-closer-defender subgame against a circular target area. In the vector field of the optimal trajectories, two singular surfaces and a singular point are observed. Each singular surface indicates a discontinuity in the closer defender’s control, while the singular point represents a situation where the target is successfully protected by a single defender. The complete solution of the two-defender game is solved based on the result of the intruder-closer-defender subgame. The proposed solution is verified through a special case where the capture range is zero. This verification also presents a simpler approach of solving the zero capture range problem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".