用神经网络结合启发式算法解决数独类谜题，效果优于纯算法方案。
Solving nonograms using Neural Networks
- 将神经网络嵌入启发式算法加速求解过程
- 组合方法在公开数据集上达到最优解率
- 首次尝试神经网络解非诺玛谜题，适合算法研究者
非诺玛（Nonograms）是根据行列数字提示对网格格子进行着色或留白的逻辑谜题。本研究对比了启发式算法、遗传算法及启发式算法与神经网络结合的方法。为训练神经网络，我们构建并公开了一个新数据集，同时开源全部算法代码。实验表明，启发式算法与神经网络的结合方案表现最佳。经文献调研，此前未有工作使用神经网络求解非诺玛，也无将神经网络与其他算法融合以加速求解的研究。
原文摘要 · Abstract (English)
Nonograms are logic puzzles in which cells in a grid must be colored or left blank according to the numbers that are located in its headers. In this study, we analyze different techniques to solve this type of logical problem using an Heuristic Algorithm, Genetic Algorithm, and Heuristic Algorithm with Neural Network. Furthermore, we generate a public dataset to train the neural networks. We published this dataset and the code of the algorithms. Combination of the heuristic algorithm with a neural network obtained the best results. From state of the art review, no previous works used neural network to solve nonograms, nor combined a network with other algorithms to accelerate the resolution process.
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