Solving nonograms using Neural Networks
This addresses a niche problem for puzzle-solving enthusiasts and researchers, but it is incremental as it builds on existing algorithms without broad impact.
The study tackled solving nonograms by analyzing heuristic, genetic, and neural network-based methods, with the combination of a heuristic algorithm and neural network achieving the best results, though no concrete numbers were provided.
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.