Bibliographic record
Abstract
In this paper we will present a study of different res-olution techniques for solving Constraint Satisfaction Problems (CSP) in the case of temporal constraints. This later problem is called Temporal Constraint Sat-isfaction Problem (TCSP). We will mainly focus here on solving TCSPs in real time and in a dynamic en-vironment. Indeed, addressing these two issues is very relevant for many real world applications. Solving a TCSP in real time is an optimization problem that we call MTCSP (Maximal Temporal Constraint Satisfac-tion Problems). The objective function to minimize is the number of temporal constraint violations. The re-sults of the tests we have performed on randomly gen-erated MTCSPs show that the approximation method Min-Conflict-Random-Walk(MCRW) is the algorithm of choice for solving MTCSPs. Comparison study of the different dynamic arc-consistency algorithms for solving dynamic temporal constraint problems in a pre-processing phase demonstrates that the new algorithm we propose and based on a recent arc-consistency al-gorithm represents a better compromise between time and space than the other dynamic arc-consistency al-gorithms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".