用低层操作数据自动识别玩家风格，减少人为偏见。
Play Style Identification Using Low-Level Representations of Play Traces in MicroRTS
- 直接从原始操作记录训练CNN-LSTM自编码器提取特征
- 在潜在空间中清晰区分不同AI玩家的对战风格
- 适合游戏设计与智能体探索多样化策略
玩家风格识别可为游戏设计提供洞见并实现自适应体验，提升游戏智能体表现。以往方法依赖领域知识构建手工特征，近期方法虽考虑操作序列结构，仍需一定程度的领域抽象。本研究探索在MicroRTS中使用无监督CNN-LSTM自编码器模型，直接从低层操作轨迹数据中学习潜在表示。结果表明，该方法能在潜在空间中有效分离不同游戏智能体，降低对领域专家知识的依赖及其带来的偏见。该潜在空间进一步用于引导对所研究AI玩家多样化玩法的探索。
原文摘要 · Abstract (English)
Play style identification can provide valuable game design insights and enable adaptive experiences, with the potential to improve game playing agents. Previous work relies on domain knowledge to construct play trace representations using handcrafted features. More recent approaches incorporate the sequential structure of play traces but still require some level of domain abstraction. In this study, we explore the use of unsupervised CNN-LSTM autoencoder models to obtain latent representations directly from low-level play trace data in MicroRTS. We demonstrate that this approach yields a meaningful separation of different game playing agents in the latent space, reducing reliance on domain expertise and its associated biases. This latent space is then used to guide the exploration of diverse play styles within studied AI players.
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