Diversifying dynamic difficulty adjustment agent by integrating player state models into Monte-Carlo tree search

Game developers have employed dynamic difficulty adjustment (DDA) in designing game artificial intelligence (AI) to improve players’ game experience by adjusting the skill of game agents. Traditional DDA agents depend on player proficiency only to balance game difficulty, and this does not always lead to improved enjoyment for the players. To improve game experience, there is a need to design game AIs that consider players’ affective states. Herein, we propose AI opponents that decide their next actions according to a player’s affective states, in which the Monte-Carlo tree search (MCTS) algorithm exploits the states estimated by machine learning models referencing in-game features. We targeted four affective states to build the model: challenge, competence, valence, and flow. The results of our user study demonstrate that the proposed approach enables the AI opponents to play automatically and adaptively with respect to the players’ states, resulting in an enhanced game experience. •Adapting real-time video game difficulty with player’s affective states-based MCTS.•DDA agent predicts the players’ state using occurred logs and simulated logs by MCTS.•DDA agent adapts game difficulty based on predicted player’s state to enhance it.•DDA strategies can be diversified by focusing on different player states.•The possibility of balancing the game difficulty while satisfying diverse preference.