Supervisor Localization: A Top-Down Approach to Distributed Control of Discrete-Event Systems
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Bibliographic record
Abstract
We study the design of distributed control for discrete-event systems (DES) in the framework of supervisory control theory. We view a DES as comprised of a group of agents, acting independently except for specifications on global (group) behavior. The central problem investigated is how to synthesize local controllers for individual agents such that the resultant controlled behavior is identical with that achieved by global supervision. In the case of small-scale DES, a supervisor localization algorithm is developed that solves the problem in a top-down fashion: first, compute a global supervisor, then decompose it to local controllers while preserving the global controlled behavior. In the case of large-scale DES where owing to state explosion a global supervisor might not be feasibly computable, a decomposition-aggregation solution procedure is developed that combines the supervisor localization algorithm with an efficient modular control theory.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it