Supervisory control of dense real-time discrete-event systems with partial observation
Bibliographic record
Abstract
In supervisory control theory, the basic task of the supervisor is to disable certain events of the plant so that the obtained behaviour lies within a given specification. We propose a method which extends this theory with the following two points. First, the plant and the specification contain temporal constraints and are described by a model called Timed Automata (TA). Second, the supervisor has only a partial observation of the behaviour of the plant. The problem that arises with the TA model is that the state space can be infinite. Recently, we proposed a method to finitely represent the state space which generates less states than the well-known region graph approach. Its principle consists of transforming a TA into a Finite State Automaton (FSA) using two special types of events: Set and Exp. Such a FSA is denoted se-FSA. In this article, we propose a method for the supervisory control of timed discrete event systems that are modelled by TA and partially observable. We use the above-mentioned transformation procedure for representing the plant and the specification by two se-FSAs. Then, we develop a procedure for generating the supervisor from the two se-FSAs that represent the plant and the specification. We also propose a supervisory control architecture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".