用遗传算法动态生成适配玩家水平的解谜游戏，提升学习体验。
From Frustration to Fun: An Adaptive Problem-Solving Puzzle Game Powered by Genetic Algorithm
- 基于遗传算法实时生成路径类谜题，自动调节难度。
- 通过记录玩家行为，使谜题难度贴近目标挑战水平。
- 适合教育场景中的自适应学习系统研究者参考。
本文提出一种由AI驱动的自适应解谜游戏，用于培养问题解决能力。系统采用遗传算法动态生成基于路径寻踪的谜题，通过在线实时方式调整每道谜题的难度，以匹配个体玩家表现。玩家建模系统记录用户交互数据，结合多种指标指导谜题生成，使其逼近预设难度目标。该方法融合程序化内容生成与在线自适应难度调节，旨在维持玩家参与度、减少挫败感，并保持最优挑战水平。一项初步用户研究评估了该方法的有效性，对比了不同自适应难度机制，并分析了玩家反馈。本工作为情感感知的玩家模型、高级AI自适应技术及游戏外教育应用的研究奠定了基础。
原文摘要 · Abstract (English)
This paper explores adaptive problem solving with a game designed to support the development of problem-solving skills. Using an adaptive, AI-powered puzzle game, our adaptive problem-solving system dynamically generates pathfinding-based puzzles using a genetic algorithm, tailoring the difficulty of each puzzle to individual players in an online real-time approach. A player-modeling system records user interactions and informs the generation of puzzles to approximate a target difficulty level based on various metrics of the player. By combining procedural content generation with online adaptive difficulty adjustment, the system aims to maintain engagement, mitigate frustration, and maintain an optimal level of challenge. A pilot user study investigates the effectiveness of this approach, comparing different types of adaptive difficulty systems and interpreting players' responses. This work lays the foundation for further research into emotionally informed player models, advanced AI techniques for adaptivity, and broader applications beyond gaming in educational settings.
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