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.

翻译：现实世界中的许多游戏通常规模过大而难以直接处理，因此需要借助游戏抽象技术将其规模缩小数个数量级。过去二十年间，游戏抽象领域取得了显著进展；然而，先前多数研究具有领域特异性（通常集中于扑克游戏），导致这些技术难以在未对目标游戏进行深入分析的情况下泛化至其他场景。本文提出一种领域无关的游戏抽象方法，该方法借鉴自然语言处理领域的词嵌入技术。通过将每个动作视为单词、游戏数据视为语料库，可训练词向量将每个动作表示为实值向量，进而通过聚类实现游戏抽象。我们还探索了基础嵌入模型的应用，并证明通过该方式获得的动作嵌入能够捕捉到关于底层游戏的惊人信息量。实验结果表明，本文提出的游戏抽象技术具有有效性，但并未超越针对特定游戏定制的专用算法。