  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.
