Timed fault-tolerant supervisory control
Bibliographic record
Abstract
In Mulahuwaish,1–4 we investigated the problem of fault tolerance in the framework of untimed discrete-event systems (DES). This approach is different from the typical fault-tolerant methodology as the approach does not rely on detecting faults and switching to a new supervisor; it requires a supervisor to work correctly under normal and fault conditions. This is a passive approach that relies upon inherent redundancy in the system being controlled. In this paper we extend the work of Mulahuwaish1–4 to the timed DES (TDES) setting. We introduce our setting, and then provide a set of timed fault tolerant definitions designed to capture different types of fault scenarios and to ensure that our system remains controllable in each scenario. As the nonblocking property is the same for timed and untimed DES, the untimed fault-tolerant nonblocking properties and algorithms from Mulahuwaish1–4 can also be used in the timed setting without any changes. We then present algorithms to verify these properties followed by complexity analyses and correctness proofs of the algorithms. An example is then provided to illustrate our approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".