A Networked Decision and Information System for increased agility in teaming unmanned combat vehicles
Bibliographic record
Abstract
A networked decision and information system (NDIS) architecture is proposed for the routing and munitions management (RMM) of multiple unmanned combat vehicles (UCVs) evolving in an imperfectly known and adversarial environment. Increased UCV teaming agility is evidenced by the online decision policy proposed in this paper. The policy exploits the multiformations capability of UCVs to group into large formations and to divide into smaller formations on their way to high-value, tactical targets. The NDIS architecture is composed of two principal components: (i) a sensory information management network (SIM-Net), which handles data from a set of mobile sensors and then determines whether sensor redundancy should and can be used, and estimates an information state vector on the locations of the adversarial ground units and decoys; (ii) the networked UCVs calculate the worst-case minimization policy for the RMM problem based on the available information state vector. NDIS adopts a distributed one-step lookahead approach, thereby enabling time-constrained approximations of the expected cost-to-go function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".