Evaluating SAT Solver Metrics as Predictors of Human-Perceived Nonogram Difficulty

2026-08-24

AI Summary

研究通过SAT求解器和用户实验评估非欧诺米难度预测指标，发现求解器指标与人类感知难度关联不大，但专家水平影响这种关系。

Abstract

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 expertise moderates the relationship between solver metrics and reported difficulty. In this process, we uncover distinct, recurring solving strategies that indicate human preference for complex propagation, diverging from solver-measured complexity.

Problem

• SAT Solver Metrics
• Human-Perceived Difficulty
• Nonograms

Innovation

• SAT solver metrics
• human-perceived difficulty
• constraint satisfaction problem
• expertise moderation
• solving strategies