Multi-agent methodology for distributed and cooperative supervisory estimation subject to unreliable information
Bibliographic record
Abstract
In this work, a novel multi-agent framework for cooperative supervisory estimation of linear time-invariant systems is proposed. This framework is developed based on the notion of subobservers and a discrete-event system (DES) supervisory control and is applicable to large-scale systems. We introduce a group of subobservers where each subobserver is estimating certain states that are conditioned on a given input, output and state information. The cooperation among the subobservers is managed by a DES supervisor. The supervisor makes decisions regarding the selection and configuration of a set of subobservers to successfully estimate all the system states, while the feasibility of the overall integrated cooperative subobservers is verified. When certain anomalies (faults) are present in the system, or the sensors and subobservers become unreliable, the supervisor reconfigures the set of selected subobservers so that the impacts of anomalies on the estimation performance are minimised to the extent that is possible. The application and capabilities of our proposed methodology in a practical industrial process is demonstrated through numerical simulations.
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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 | 0.000 |
| 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 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".