Decentralized Supervisory Control of Discrete Event Systems: Using Multi-Decision Control as an Alternative to Inference-Based Control
Bibliographic record
Abstract
Some years ago, a decentralized architecture, qualified as inference-based, has been developed for supervisory control of discrete event systems. One of its essential principles is to associate ambiguity levels to local decisions. More recently, a decentralized control architecture, qualified as multi-decision, has been developed. Its main principle is to use several decentralized control architectures in parallel. So far, multi-decision control has been mostly studied as a solution to generalize inference-based control, by using several inference-based architectures in parallel. In the present study, we regard multi-decision control with a different perspective. Instead of using multi-decision control to generalize inference-based control, we will rather use it as an alternative to inference-based control. More precisely, our objective is to avoid using inference-based architectures, by using instead several simpler architectures running in parallel.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".