Symbolic Synthesis and Verification of 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. The current HISC verification and synthesis algorithms are based upon explicit state and transition listings which limit the size of a given level to about 107states when 1GB of memory is used. In this paper, we extend the HISC approach by introducing a set of predicate based fixed point operators that allow us to do a per level synthesis to construct for each level a maximally permissive supervisor that satisfies the corresponding HISC conditions. We prove that these fixpoint operators compute the required level-wise supremal languages. We then present algorithms that implement the fixpoint operators. Based on these algorithms, a symbolic implementation is briefly discussed which can be implemented using binary decision diagrams. We also discuss a method to implement our synthesized supervisors in a more compact manner. A large manufacturing system example (worst case state space on the order of 1030) extended from the ALP example is briefly discussed. The example showed that we can now handle a given level with a statespace as large as 1015states, using less than 160MB of memory. This represents a significant improvement in the size of systems that can be handled by the HISC approach. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".