Neural Networks Learning the Concept of Influence in Go
Santos, Gabriel Machado (Federal University of Uberlândia) | Julia, Rita Maria Silva (Federal University of Uberlândia) | Saito, Marco (Federal University of Uberlândia) | Aguiar, Matheus Araujo (Instituto Federal do Triangulo Mineiro)
This paper describes an intelligent agent that uses a MLP (Multi-Layer Perceptron) Neural Network (NN) in order to evaluate a game state in the game of Go based, exclusively, in an influence analysis. The NN learns the concept of Influence, which is domain specific to the game of Go. The learned function is used to evaluate board states in order to predict which player will win the match. The results show that, in later stages, the NN can achieve an accuracy of up to 89.3% when predicting the winner of the game. As future work the authors propose the incorporation of several improvements to the NN and also its integration intelligent player agents for the game of go, such as Fuego and GnuGo.
