On the complexity of supervisory control design in the RW framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The time complexity of supervisory control design for a general class of problems is studied. It is shown to be very unlikely that a polynomial-time algorithm can be found when either (1) the plant is composed of m components running concurrently or (2) the set of legal behaviors is given by the intersection of n legal specifications. That is to say, in general, there is no way to avoid constructing a state space which has size exponential in m+n. It is suggested that, rather than discouraging future work in the field, this result should point researchers to more fruitful directions, namely, studying special cases of the problem, where certain structural properties possessed by the plant or specification lend themselves to more efficient algorithms for designing supervisory controls. As no background on the subject of computational complexity is assumed, we have tried to explain all the borrowed material in basic terms, so that this paper may serve as a tutorial for a system engineer not familiar with the subject.
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.
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 | 0.002 | 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 | 0.001 |
| 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