Optimal distributed and cooperative supervisory estimation of multi-agent systems subject to unreliable information
Bibliographic record
Abstract
In this work, a novel framework for optimal cooperative supervisory estimation of multi-agent linear time-invariant (LTI) systems is proposed which is applicable to a large class of multi-agent systems. This framework was recently developed by the authors based on the notion of sub-observers and a discrete-event system (DES) supervisory control. Each sub-observer estimates certain states that are conditioned on given inputs, outputs, and states information. Moreover, the cooperation among the sub-observers is managed by a DES supervisor. In this work, our proposed supervisory estimation framework is extended to the combinatorial optimization domain. When certain anomalies (faults) are present in the system, or the sensors and sub-observers become unreliable, the proposed optimal DES supervisor makes decisions regarding the selection and reconfiguration of sets of sub-observers to estimate all the system states, while simultaneously a performance index that incorporates the communication cost, computation cost, and reconfiguration cost, and the number of invalid state estimates is minimized. The application 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".