  Artificial intelligence (AI) has achieved superhuman performance in board
games such as Go, chess, and Othello (Reversi). In other words, the AI system
surpasses the level of a strong human expert player 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 learns and plays a
game the same way as AlphaZero, but can change its skills. AlphaDDA estimates
the value of the game state from only the board state using the DNN. AlphaDDA
changes a parameter dominantly controlling its skills according to the
estimated value. Consequently, AlphaDDA adjusts its skills according to a game
state. 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 with other AI agents. 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 based on an accurate
estimation of the value from the state of a game. We believe that the AlphaDDA
approach for DDA can be used for any game AI system if the DNN can accurately
estimate the value of the game state and we know a parameter controlling the
skills of the AI system.
