A distributed and cooperative supervisory estimation of multi-agent systems - Part I: Framework
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
In this work, we propose a framework for supervisory cooperative estimation of multi-agent linear time-invariant (LTI) systems. We introduce a group of sub-observers, each estimating certain states that are conditioned on given input, output, and state information. The cooperation among the sub-observers is supervised by a discrete-event system (DES) supervisor. The supervisor makes decisions on selecting and configuring a set of sub-observers to successfully estimate all states of the system. Moreover, when certain anomalies are present, the supervisor reconfigures the set of selected sub-observers so that the impact of anomalies on the estimation performance is minimized. This framework is applicable to any multi-agent system including large-scale industrial processes. In this paper (Part I), our proposed framework for supervisory estimation is developed based on the notion of sub-observers and DES supervisory control. In the companion paper (Part II), a DES-based combinatorial optimization method for selection of an optimal set of sub-observers is presented, the feasibility of the overall integrated sub-observers is validated, and the application of our proposed method in a practical industrial process is demonstrated through numerical simulations.
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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 | 0.001 |
| 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