用少量数据模拟用户认知，为个性化游戏设计提供精准支持
CogSimulator: A Model for Simulating User Cognition & Behavior with Minimal Data for Tailored Cognitive Enhancement
- 基于Wasserstein-1距离与坐标搜索优化，实现小样本下认知行为模拟
- 在Wordle数据集上优于多数传统模型，三项指标均表现更优
- 适合教育游戏开发者与认知增强研究者快速验证个性化设计
认知与游戏的互动，特别是教育类游戏对认知能力的提升，近年来备受关注。本文提出CogSimulator，一种在小群体场景中以极低数据量模拟用户认知与行为的新算法，以单词游戏Wordle为例进行验证。该模型采用一阶Wasserstein距离与坐标搜索优化进行超参数调优，实现在新游戏情境下的高精度少样本预测。与多个传统机器学习模型相比，基于Wordle数据集的对比实验显示，本模型在平均Wasserstein-1距离、均方误差和平均准确率三项指标上均表现更优，验证了其在个性化认知增强游戏设计中的有效性。
原文摘要 · Abstract (English)
The interplay between cognition and gaming, notably through educational games enhancing cognitive skills, has garnered significant attention in recent years. This research introduces the CogSimulator, a novel algorithm for simulating user cognition in small-group settings with minimal data, as the educational game Wordle exemplifies. The CogSimulator employs Wasserstein-1 distance and coordinates search optimization for hyperparameter tuning, enabling precise few-shot predictions in new game scenarios. Comparative experiments with the Wordle dataset illustrate that our model surpasses most conventional machine learning models in mean Wasserstein-1 distance, mean squared error, and mean accuracy, showcasing its efficacy in cognitive enhancement through tailored game design.
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