A Networked Decision and Information System for increased agility in teaming unmanned combat vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
| 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