AN EFFICIENT LOTOS-BASED FRAMEWORK FOR DESCRIBING AND SOLVING (TEMPORAL) CSPs
Bibliographic record
Abstract
Simulation of complex Lotos specifications is not always efficient due to the space explosion problem of their corresponding transition systems. To overcome this difficulty in practice, we present in this paper a novel approach which integrates constraint propagation techniques into the Lotos specifications. These solving techniques are used to reduce the size of the search space before and during the search for a solution to a given combinatorial problem under constraints. In order to do that, we first tackle the challenging task of describing combinatorial problems in Lotos using the Constraint Satisfaction Problem (CSP) framework. In this regard, we provide two generic Lotos templates for describing CSPs and temporal CSPs (CSPs involving temporal constraints). To evaluate the time performance of the framework we propose, we have conducted several experimental tests on instances of the N-Queens, the machine scheduling and randomly generated CSPs. The results of these experiments are promising and demonstrate the efficiency of Lotos simulation when CSP techniques are integrated.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".