Application of Different Artificial Intelligence Methods on Reversi

Abstract

Using different algorithms in artificial intelligence to perform adversarial agent games. The most popular adversarial game that could be implemented using artificial intelligence algorithms is chess games. The algorithm this research used includes minimax with improvement and deep reinforcement learning. The goal for this research is to compute the popular game Reversi in different artificial intelligence methods successfully. Moreover, the research seeks for improvements in the heuristic part of minimax algorithm and the combination of deep reinforcement learning with Monte Carlo Tree with Neural Network. This paper uses Reversi as an example to analyze different algorithms, including Minimax, Monte Carlo Tree, and Neural Networks. As a result, both algorithms work successfully. The alpha-beta pruning minimax algorithm with improvement in heuristic function and fixed depth cut-off significantly increase the winning probability and time cost of our artificial intelligence agent. The deep reinforcement learning successfully combined MCTS with neural network to train two agents to complete Reversi with great winning probability.