Large Language Models (LLMs), trained solely on massive text data, have achieved high performance on the Winograd Schema Challenge (WSC), a benchmark proposed to measure commonsense knowledge and reasoning abilities about the real world. This suggests that the language produced by humanity describes a significant portion of the world with considerable nuance. In this study, we attempt to harness the high expressive power of language within cellular automata. Specifically, we express cell states and rules in natural language and delegate their updates to an LLM. Through this approach, cellular automata can transcend the constraints of merely numerical states and fixed rules, providing us with a richer platform for simulation. Here, we propose LOGOS-CA (Language Oriented Grid Of Statements - Cellular Automaton) as a natural framework to achieve this and examine its capabilities. We confirmed that LOGOS-CA successfully performs simple forest fire simulations and also serves as an intriguing subject for investigation from an Artificial Life (ALife) perspective. In this paper, we report the results of these experiments and discuss directions for future research using LOGOS-CA.

翻译：仅在海量文本数据上训练的大型语言模型（LLMs）在Winograd模式挑战（WSC）上取得了高性能表现，该基准旨在衡量关于现实世界的常识知识与推理能力。这表明人类产生的语言以相当精细的粒度描述了世界的很大一部分。在本研究中，我们尝试在元胞自动机中利用语言的高表达能力。具体而言，我们用自然语言表达细胞状态与规则，并将其更新委托给一个LLM。通过这种方法，元胞自动机可以超越仅具有数值状态和固定规则的限制，为我们提供一个更丰富的模拟平台。在此，我们提出LOGOS-CA（面向语言的陈述网格-元胞自动机）作为实现此目标的自然框架，并考察其能力。我们证实LOGOS-CA能够成功执行简单的森林火灾模拟，并且从人工生命（ALife）的视角来看，它也是一个引人入胜的研究对象。本文中，我们报告了这些实验的结果，并讨论了使用LOGOS-CA进行未来研究的方向。