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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".