Solving the puzzle problem using Hopfield neural network in conjunction with tree search algorithm

In this paper, a new approach based on Artificial Neural Networks for solving the Puzzle Problem in conjunction with the Tree Search Algorithm, is presented. For this purpose, a Hopfield Neural Network is used in a certain constraint satisfaction problem of the puzzle so that the energy of a state can be interpreted as the extent to which a hypothesis fits the underlying neural formulation model. Thus, low energy values indicate a good level of constraint satisfaction of the puzzle problem. Also, another criterion known as "Tree Search Algorithm", is used to solve the puzzle problem. At the end, based on the appropriate behaviors of each-of the presented algorithms, these two algorithms are combined so that they generate a much more powerful algorithm than each of them individually. Finally, a comparison is made for the actual performance of the proposed algorithm and the Hopfield Neural Network optimizer formerly presented in [12].