# Domain-Independent Game Abstraction using Word Embedding Techniques Source: [Domain-Independent Game Abstraction using Word Embedding Techniques]() Collection index: [llms.txt]() Data updated: 2026-05-19T05:00:05.916Z Authors: Juho Kim, Tuomas Sandholm Published: 2026-05-15T02:30:36.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Exploitability Computation on 256-Kuhn Poker (full)](): Exploitability computation; 256-Kuhn poker (full); Exploitability: 0.007. ## Original sources and implementation [Original paper]() [PDF]()