Auditor Product and Controller Synthesis for Nondeterministic Transition Systems With Practical LTL Specifications
Bibliographic record
Abstract
Controller design for continuous systems with linear temporal logic (LTL) specifications is a computationally intensive task. Abstracting a discrete transition system from a real-world continuous-state system often results in a state machine with a large number of states and nondeterministic transitions. This makes controller synthesis for LTL specifications difficult specially when the design specification is lengthy. To reduce the complexity, we consider the specifications that are in the conjunctive form of practical LTL patterns. We use auditor product to incrementally restrict the system to satisfy the safety part of each subspecification. The control strategy, that satisfies the liveness part is then calculated by solving a generalized Buchi game on the result of the auditor product of the discrete transition system with all subspecifications. This approach has the same worst case computational complexity as GR(1) synthesis, but avoids some of the fundamental limitations involved with Assumption → Guarantee formulation of the problem.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".