The Model and Algorithm to Estimate the Difficulty Levels of Sudoku Puzzles
Bibliographic record
Abstract
Sudoku is a number placement mathematical puzzle based on logic. The purpose of this paper is to discuss suitablemodels and algorithm to generate Sudoku puzzles of varying difficulty. It is generally recognized that hand-made puzzlesare more enjoyable than those generated by computer. Our goal is to establish models to generate Sudoku puzzles ofvarying difficulty, which are as enjoyable as hand-made ones. As we believe that puzzles generated by simulating thedesign process of hand-made ones will also be of much enjoyment, we established our first model -No Brute-Force.Brute-Force technique is excluded from this algorithm, for there is no enjoyment in solving puzzles using it. We haveimplemented the algorithm with a JAVA program. We conclude that it is possible and reasonable to generate Sudokupuzzles of varying difficulty as enjoyable as hand-made ones.
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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 | 0.004 | 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) | 0.000 | 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".