AI 中文总结
研究如何准确建模玩家体验以设计益智游戏，提出通用表示减少特征工程维护需求，通过比较BERT、GAT与CNN基线进行实验，发现新方法在挑战性棋盘配置上性能更优，凸显通用表示优势。
AI 中文摘要
准确建模和理解玩家体验对设计引人入胜的益智游戏至关重要。常见方法是收集多样用户数据训练预测性游戏测试模型来模仿玩家行为。但现有数据驱动方法常无法捕捉玩家策略全貌，且需大量特征工程和网络架构建模。新游戏机制或功能引入时，该局限尤其明显。为应对这些挑战，我们提出一种更通用的表示，减少甚至消除持续特征工程维护需求。具体研究了两种通用网络架构：基于Transformer的模型（BERT）和图注意力模型（GAT），二者旨在有效捕捉《糖果粉碎传奇》（CCS）游戏板的关系结构。实验将这些方法与卷积神经网络（CNN）基线比较，发现在具有挑战性的棋盘配置上性能更好，凸显了通用表示的优势。
英文摘要
Accurately modeling and understanding player experience is crucial for designing engaging puzzle games. To achieve this, a common approach involves collecting diverse user data to train predictive playtesting models that mimic player behavior. However, existing data-driven methods often lack the ability to capture the full range of player strategies and require extensive feature engineering and network architecture modeling. This limitation becomes particularly evident when new game mechanics or features are introduced, which necessitate continual adjustments to the models. To addrss these challenges, we propose a more generalized representation that reduces - or even eliminates - the need for ongoing feature-engineering maintenance. Specifically, we investigate two general-purpose network architectures: (a) a transformer-based model (BERT) and (b) a graph attention model (GAT), both of which are designed to effectively capture the relational structure of Candy Crush Saga (CCS) game boards. Our experiments compare these approaches to Convolutional Neural Networks (CNN) baselines, revealing better performance on challenging board configurations and underscoring the benefits of our generalizable representation.
Comments2025 IEEE Conference on Games (CoG)
Journal refProceedings of the 2025 IEEE Conference on Games (CoG), pp. 1-8
