Comparison of methods for supervisory control and submodule construction
Bibliographic record
Abstract
Summary form only given. Over the last 25 years, methods for supervisory control of discrete event systems and methods for submodule construction based on state machine specifications have been developed quite independently by different research communities. The purpose of this paper is to give a summary of the results in these two areas and to point out the many similarities and certain differences between the approaches taken by these two communities. The basic problem, in both cases, is to find the behavior of a single submodule X such that combined with a given submodule C, this composition exhibits a behavior that conforms to a given specification S. In the case of supervisory control, the submodule C is an existing system that is to be controlled by the controller X in such a manner that a behavior compatible with S is obtained. We discuss the main issues that must be addressed for solving this problem, review certain conditions for the existence of a solution, and present the major solution algorithms. We also discuss the different treatment of allowed and required behavior, and the difficulties that arise in the context of different communication paradigms (for instance, distinguishing controllability, observability, input/output, synchronous and asynchronous communication) and different specification formalisms.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".