A distributed and cooperative supervisory estimation of multi-agent nonlinear systems
Why this work is in the frame
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Bibliographic record
Abstract
In this work, we propose a framework for supervisory cooperative estimation of multi-agent nonlinear systems. We introduce a group of sub-observers, each estimating certain states conditioned on certain given input, output, and state information. The cooperation among the sub-observers is supervised by a discrete-event system (DES). The supervisor makes decisions on selecting and configuring a set of sub-observers, so that the overall integrated sub-observers are able to successfully estimate all the states of the system. In cases when certain changes in the uncertainties take place, the supervisor reconfigures the set of selected sub-observers so that the impact of these uncertainties on the estimation performance is minimized. Our proposed method is applied to a nonlinear industrial process, and the simulations results obtained validate our analytical work.
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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