Diversifying dynamic difficulty adjustment agent by integrating player state models into Monte-Carlo tree search
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Game artificial intelligence,Dynamic difficulty adjustment,Monte-Carlo tree search,Human-centered,Machine learning,Player modeling
论文评审过程：Received 2 February 2022, Revised 17 May 2022, Accepted 27 May 2022, Available online 3 June 2022, Version of Record 11 June 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117677
