用词嵌入技术实现跨游戏的通用抽象，无需针对每类游戏重新设计。
Domain-Independent Game Abstraction using Word Embedding Techniques
- 将动作视为词汇，游戏数据当作语料训练词向量
- 在多种游戏中实现有效抽象，保留关键博弈结构
- 适合快速适配新游戏，尤其对缺乏先验知识的场景
现实世界中的许多游戏规模巨大，难以直接求解，因此需要通过游戏抽象大幅压缩其规模。过去二十年虽有诸多进展，但多数方法局限于特定领域（如扑克），难以泛化到其他类型游戏，需针对每类游戏进行深入分析。本文提出一种无需领域知识的游戏抽象方法，利用自然语言处理中的词嵌入技术：将每个动作视为一个“词”，把游戏行为数据当作“语料库”，训练得到动作的实值向量表示，再通过聚类实现抽象。我们还探索了基础嵌入模型的效果，发现所生成的动作向量能捕捉大量关于游戏本质的信息。实验表明，该方法在多种游戏中均具有效性，尽管尚未超越针对特定游戏优化的专用算法。
原文摘要 · Abstract (English)
Many games of interest in the real world are often intractably large, thereby necessitating the use of game abstraction to shrink them in size, typically by many magnitudes. Over the last two decades, there have been significant advances in game abstraction; however, the domain-specific nature (usually poker) of much of the prior work prevents those techniques from being easily generalized to other settings without extensively analyzing the game at hand. In this paper, we propose a domain-independent approach to game abstraction, which applies word embedding techniques from the field of natural language processing. Treating each action as a word and gameplay data as a corpus, word vectors can be trained to represent each action as a real-valued vector, which can then be clustered to facilitate game abstraction. We also explore the use of foundational embedding models and show that action embeddings obtained this way can capture a surprising amount of information about the underlying game. Experimental results demonstrate that our proposed game abstraction technique is effective, although it does not outperform specialized algorithms tailored to specific games.
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