#$%^&*AU2018101314A420181011.pdf#####ABSTRACT This invention is a dual self-mode that combines the neural network and the Monte Carlo tree search. This model competes with itself as a simulation of playing against an actual person as a self-promoting process. In the neural network, we adopt a brand new combination of calculations that performs the value network to analyze the chess states, and the policy network to select the position of next chess piece. The function of the Monte Carlo tree search in this model is to utilize the Monte Carlo to assess the value of every state in the tree. This model effectively dramatically reduced the time consumed because when the neural network is being combined with the Monte Carlo tree search, the neural network will automatically select the optimal method to perform the simulation which enables the model to avoid Monte Carlo tree accordingly so the model does not need to simulate the entire process of each game in order to achieve a resourceful result. After finishing all the steps in the loop, the model will restart the loop from the beginning to initial the whole process again. As a result, each very next step is different from the previous one since the model is constantly improving itself through the process of repeating the loop.
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