Generalizing the Decentralized Control of Fuzzy Discrete Event Systems
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Bibliographic record
Abstract
The main objective of this paper is to establish a general architecture for decentralized supervision of fuzzy discrete event systems (FDES). First, two different types of decentralized supervisory control architectures of FDES are presented, which fuse the locally enabled degrees of fuzzy events using the fuzzy-intersection operator and the fuzzy-union operator, respectively. Both of these architectures possess limitations in information association. Second, to overcome the aforementioned drawbacks, a general architecture for decentralized supervisory control of FDES is introduced, in which the decisions of local supervisors are fused by using both fuzzy-union and fuzzy-intersection operators. The proposed general architecture is then implemented to control a tightly coupled multirobot object manipulation task in simulation. A performance evaluation is performed to quantitatively estimate the validity of the proposed architecture compared with centralized FDES-based and decentralized crisp DES-based approaches.
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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 | 0.002 | 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