用BERT和GAT分析玩家行为，提升谜题游戏测试的通用性。
Comparative Analysis of GAT and BERT for Human-Like Playtesting
- 采用BERT与GAT捕捉糖果传奇棋盘关系结构，无需持续人工特征工程。
- 在复杂棋盘配置上表现优于CNN基线模型，验证了通用表征的有效性。
- 适合需要快速适配新游戏机制的智能测试系统开发者参考。
准确建模与理解玩家体验对设计引人入胜的谜题游戏至关重要。现有数据驱动方法常难以捕捉玩家策略全貌，且依赖大量特征工程与网络架构调整，尤其在引入新游戏机制时需持续修改模型。为此，本文提出一种更通用的表征方式，减少甚至消除对人工特征工程的依赖。我们对比了两种通用网络架构：基于Transformer的BERT与图注意力网络GAT，二者均旨在有效捕捉《糖果传奇》（Candy Crush Saga, CCS）棋盘的关联结构。实验结果表明，相较于卷积神经网络（CNN）基线，在复杂棋盘配置下表现更优，凸显了该通用表征的优势。
原文摘要 · Abstract (English)
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.
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