Stochastic, Hierarchical, Adaptive, Real-time Control (SHARC) of Automa-Teams for Tactical Military Operations
Bibliographic record
Abstract
As part of DARPA/IXO’s Mixed-Initiative Control of Automa-Teams (MICA) program, the SHARC effort is focused on enabling the collective power of automa-teams, i.e. teams of semi-autonomous vehicles, for low-cost/high-tempo tactical military operations. Given that the chief barrier to practical automatic control of distributed semi-autonomous teams (both in the air and on the ground) is complexity, a hierarchical battlespace management approach that exploits time-scale, functional, and spatial separation is adopted. In this hierarchical control framework, a spectrum of solutions that invoke different assumptions about optimality, fidelity, uncertainty, and human interaction is being developed. Accordingly, these control techniques are being developed using a spiral development process that is driven by challenge problems that gradually increase the complexity of the trade space of operational fidelity, mathematical sophistication, and computational complexity. In this paper, an overview of the MICA-SHARC program is presented followed by a sample of initial experimental results.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".