10. Evolutionary neural network model for recognizing strategic fitness of a finished Tic-Tac-Toe game

Title(KR)

• 종료된 삼목게임의 전략 적합도 인식을 위한 진화신경망 모델

Title(ENG)

• Evolutionary neural network model for recognizing strategic fitness of a finished Tic-Tac-Toe game

Keywords(KR)

• Evolutionary model, Back-propagation neural network, Genetic algorithm, Tic-Tac-Toe game, Computer Go, Strategic fitness, BPNN, GANN

Keywords(ENG)

• Evolutionary model, Back-propagation neural network, Genetic algorithm, Tic-Tac-Toe game, Computer Go, Strategic fitness, BPNN, GANN

Author

• Byung-Doo Lee

Abstract(ENG)

Evolutionary computation is a powerful tool for developing computer games. Back-propagation neural network(BPNN) was proved to be a universal approximator and genetic algorithm(GA) a global searcher. The game of Tic-Tac-Toe, also known as Naughts and Crosses, is often used as a test bed for testing new AI algorithms. We tried to recognize the strategic fitness of a finished Tic-Tac-Toe game when the parameters, such as a sequence of moves, its game depth and result, are provided. To implement this, we've constructed an evolutionary model using GA with back-propagation NNs(GANN). The experimental results revealed that GANN, in the very long training time, converges very slowly; however, performance of recognizing the strategic fitness does not meet we expected and, further, increase of the population size does not significantly contribute to the performance of GANN.

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