用分词+距离度量，通用表示游戏轨迹并实现高效分类。
JSON-Bag: A generic game trajectory representation
- 将游戏轨迹的JSON描述分词后建模为向量，用JSD衡量相似性。
- 在6个桌游上对3类任务分类准确率超人工特征基线，支持N-shot学习。
- 可自动提取有效特征，适用于策略差异分析与小样本场景。
我们提出JSON-Bag模型，通过分词游戏轨迹的JSON描述并使用Jensen-Shannon距离（JSD）进行表示。基于原型的最近邻搜索（P-NNS）在六个桌游（7 Wonders、Dominion、Sea Salt and Paper、Can't Stop、Connect4、Dots and boxes）上评估了该方法在三类轨迹分类任务中的有效性：区分玩家代理、游戏参数或生成轨迹的种子。结果表明，该方法在多数任务中优于基于手工特征的基线。N-shot分类实验显示，使用JSON-Bag原型表示轨迹类别具有良好的样本效率。此外，通过将分词视为独立特征输入随机森林，实现了对表现较差任务的显著精度提升。最后，在所有六款游戏中，不同玩家类别原型间的JSD值与实际策略距离高度相关。
原文摘要 · Abstract (English)
We introduce JSON Bag-of-Tokens model (JSON-Bag) as a method to generically represent game trajectories by tokenizing their JSON descriptions and apply Jensen-Shannon distance (JSD) as distance metric for them. Using a prototype-based nearest-neighbor search (P-NNS), we evaluate the validity of JSON-Bag with JSD on six tabletop games: 7 Wonders, Dominion, Sea Salt and Paper, Can't Stop, Connect4, Dots and boxes; each over three game trajectory classification tasks: classifying the playing agents, game parameters, or game seeds that were used to generate the trajectories. Our approach outperforms a baseline using hand-crafted features in the majority of tasks. Evaluating on N-shot classification suggests using JSON-Bag prototype to represent game trajectory classes is also sample efficient. Additionally, we demonstrate JSON-Bag ability for automatic feature extraction by treating tokens as individual features to be used in Random Forest to solve the tasks above, which significantly improves accuracy on underperforming tasks. Finally, we show that, across all six games, the JSD between JSON-Bag prototypes of agent classes highly correlates with the distances between agents' policies.
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