AlphaDDA: Game artificial intelligence with dynamic difficulty adjustment using AlphaZero

Artificial intelligence (AI) has achieved superhuman performance in board games such as Go, chess, and Othello (Reversi). In other words, AI has become too strong an opponent for human players in such games. In this context, it is difficult for a human player to enjoy playing the games with the AI. To keep human players entertained and immersed in a game, the AI is required to dynamically balance its skill with that of the human player. To address this issue, we propose AlphaDDA, an AlphaZero-based AI with dynamic difficulty adjustment (DDA). AlphaDDA consists of a deep neural network (DNN) and a Monte Carlo tree search, as in AlphaZero. AlphaDDA estimates the value of the game state from only the board state using the DNN and changes its skill according to the value. AlphaDDA can adjust its skill using only the state of a game without any prior knowledge regarding an opponent. In this study, AlphaDDA plays Connect4, Othello, and 6x6 Othello, which is Othello using a 6x6 size board, with other AI agents. The other AI agents are AlphaZero, Monte Carlo tree search, the minimax algorithm, and a random player. This study shows that AlphaDDA can balance its skill with that of the other AI agents, except for a random player. The DDA ability of AlphaDDA is derived from an accurate estimation of the value from the state of a game. We believe that the AlphaDDA approach can be used for any game in which the DNN can estimate the value from the state.