Synthesis Method for Hierarchical Interface-based Supervisory Control
Bibliographic record
Abstract
Hierarchical Interface-based Supervisory Control (HISC) decomposes a discrete-event system (DES) into a high-level subsystem which communicates with n ges 1 low- level subsystems, through separate interfaces which restrict the interaction of the subsystems. It provides a set of local conditions that can be used to verify global conditions such as nonblocking and controllability. As each clause of the definition can be verified using a single subsystem, the complete system model never needs to be stored in memory, offering potentially significant savings in computational resources. Currently, a designer must create the supervisors himself and then verify that they satisfy the HISC conditions. In this paper, we develop a synthesis method that can take advantage of the HISC structure. We replace the supervisor for each level by a corresponding specification DES. We then do a per level synthesis to construct for each level a maximally permissive supervisor that satisfies the corresponding HISC conditions. We define a set of language based fixpoint operators and show that they compute the required level-wise supremal languages. We then discuss the complexity of the algorithms that we have constructed that implement the fixpoint operators and show that they potentially offer significant improvement over the monolithic approach. A large manufacturing system example (estimated worst case statespace on the order of 1022) extended from the AIP example is discussed. A software tool for synthesis and verification of HISC systems using our approach was also developed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".