Dynamic difficulty adjustment (DDA) is a set of techniques that aim to automatically adapt the difficulty of a video game based on the player’s performance. This article presents a methodology for DDA using ideas from the theory of flow and case-based reasoning (CBR). In essence, we are looking to generate game sessions with a similar difficulty evolution to previous game sessions that have produced flow in players with a similar skill level. We propose a CBR approach to dynamically assess the player’s skill level and adapt the difficulty of the game based on the relative complexity of the last game states. We develop a DDA system for Tetris using this methodology and show, in an experiment with 40 participants, that the DDA version has a measurable impact on the perceived flow using validated questionnaires.

中文翻译：

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在俄罗斯方块中动态诱导流动的测量控制

动态难度调整 (DDA) 是一组技术，旨在根据玩家的表现自动调整视频游戏的难度。本文介绍了一种使用流理论和基于案例推理 (CBR) 的思想进行 DDA 的方法。从本质上讲，我们正在寻求生成与之前的游戏会话具有相似难度演变的游戏会话，这些游戏会话已经在具有相似技能水平的玩家中产生了流量。我们提出了一种 CBR 方法来动态评估玩家的技能水平并根据最后游戏状态的相对复杂性调整游戏的难度。我们开发了一个 DDA 系统俄罗斯方块使用这种方法，并在 40 名参与者的实验中表明，DDA 版本使用经过验证的问卷对感知流具有可衡量的影响。