Decentralized Supervisory Control of Discrete Event Systems - Moving Decisions Closer to Actions
Bibliographic record
Abstract
In decentralized control of discrete event systems, two main agents contribute to the computation of decisions: local supervisors and fusion modules. The local supervisors process the information detected on the plant and its environment, and transmit their results to the fusion modules. The latter process what is received from the local supervisors in order to decide actions to be applied to the plant. In the existing decentralized control architectures, the local supervisors execute complex operations, while the fusion modules execute simple operations. In the present article, we propose to move the decision computation complexity from local supervisors to fusion modules, that is what we term: moving decisions closer to actions. We justify this movement of decision and develop a simple architecture based on it. With the proposed architecture, the local supervisors are simple local observers, while all decisions are computed by the fusion modules. We characterize the class of languages achievable with the new architecture and compare it with the classes of languages achievable with the existing decentralized architectures and the centralized architecture.
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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 | 0.001 | 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".