Multi-decision decentralized control of discrete event systems : Application to the C&P architecture
Bibliographic record
Abstract
This article deals with decentralized supervisory control, where a set of supervisors cooperate in order to control a plant. We propose a new control framework where each supervisor issues a tuple of so called micro-decisions, instead of a single decision for a controllable event. The proposed approach is called multi-decision supervisory control and is intended to be applicable to any existing decentralized architecture in order to generalize the latter. In this paper, we demonstrate the applicability of the new framework to the conjunctive and permissive (C&P) architecture. The obtained architecture is naturally called C&P multidecision architecture. We define and study the notion of C&P m-coobservability, which is useful to characterize the class of achievable languages. Since C&P m-coobservability seems to be undecidable, we finally propose a stronger and obviously decidable version of C&P m-coobservability.
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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 | 0.001 |
| 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 | 0.001 |
| 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".