Learning in a Complex Adaptive System for ISR Resource Management
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
The US DOD is committed to improving the effectiveness of the collective set of ISR assets in generating mission relevant information. Their vision is of a selfsynchronizing, horizontally integrated ‘swarm’ of sensors, processing elements and communication assets that autonomously organize to meet the dynamically evolving ISR needs of future missions/campaigns. This paper describes an analysis workstation that is being constructed to support the evaluation of alternative designs for such a system. Within the workstation a complex adaptive system models the dynamic formation of collection teams while evolutionary algorithms, operating at the agent level, attempt to optimize the global performance of the ISR family of systems by modifying the various agent’s responsiveness to attributes of the ‘support requests’. Joint MEASURE, a DEVS based Monte Carlo mission effectiveness simulator developed by Lockheed Martin, provides the infrastructure for this workstation enabling the unpredictability of scenario evolution to be an integral element of the optimized solution. An overview of the architecture is provided along with sample results and lessons learned.
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 | 0.000 | 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