The Light-Up puzzle, also known as the AKARI puzzle, has never been solved using modern artificial intelligence (AI) methods. Currently, the most widely used computational technique to autonomously develop solutions involve evolution theory algorithms. This project is an effort to apply new AI techniques for solving the Light-up puzzle faster and more computationally efficient. The algorithms explored for producing optimal solutions include hill climbing, simulated annealing, feed-forward neural network (FNN), and convolutional neural network (CNN). Two algorithms were developed for hill climbing and simulated annealing using 2 actions (add and remove light bulb) versus 3 actions(add, remove, or move light-bulb to a different cell). Both hill climbing and simulated annealing algorithms showed a higher accuracy for the case of 3 actions. The simulated annealing showed to significantly outperform hill climbing, FNN, CNN, and an evolutionary theory algorithm achieving 100% accuracy in 30 unique board configurations. Lastly, while FNN and CNN algorithms showed low accuracies, computational times were significantly faster compared to the remaining algorithms. The GitHub repository for this project can be found at https://github.com/rperera12/AKARI-LightUp-GameSolver-with-DeepNeuralNetworks-and-HillClimb-or-SimulatedAnnealing.

中文翻译：

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用人工智能点亮 Light Up

Light-Up 谜题，也称为 AKARI 谜题，从未使用现代人工智能 (AI) 方法解决。目前，最广泛使用的自主开发解决方案的计算技术涉及进化论算法。该项目致力于应用新的 AI 技术，以更快、更高效地解决 Light-up 难题。为产生最佳解决方案而探索的算法包括爬山、模拟退火、前馈神经网络 (FNN) 和卷积神经网络 (CNN)。开发了两种算法用于爬山和模拟退火，使用 2 个动作（添加和移除灯泡）和 3 个动作（添加、移除或将灯泡移动到不同的单元格）。爬山和模拟退火算法在 3 个动作的情况下都显示出更高的准确性。模拟退火显示明显优于爬山、FNN、CNN 和进化理论算法，在 30 种独特的电路板配置中实现了 100% 的准确率。最后，虽然 FNN 和 CNN 算法的准确度较低，但与其余算法相比，计算时间明显更快。该项目的 GitHub 存储库位于 https://github.com/rperera12/AKARI-LightUp-GameSolver-with-DeepNeuralNetworks-and-HillClimb-or-SimulatedAnnealing。