Evaluating SAT Solver Metrics as Predictors of Human-Perceived Nonogram Difficulty
Algorithmic solver effort is often assumed to align with perceived puzzle difficulty, but this assumption is rarely tested against human solving data. We evaluate this assumption for Nonograms, a popular logic puzzle similar to Sudoku in which numeric clues along each row and column determine a unique solution grid. We formulate Nonograms as a constraint satisfaction problem and solve them using existing SAT solvers. We then conduct a user study in which we collect data on both participant interactions and reported difficulty. We find that neither participants' reported difficulty nor their behavioural signals correlate meaningfully with SAT solver metrics; however, we find evidence that ...
| Authors | Changdao He, Yibing Ju, Jonathan Calver et al. |
|---|---|
| Primary category | cs.HC |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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