用自然语言定义元胞自动机状态与规则，实现更丰富的模拟系统。
LOGOS-CA: A Cellular Automaton Using Natural Language as State and Rule
- 以自然语言作为元胞状态和更新规则，由大模型驱动演化
- 成功模拟森林火灾，展现语言驱动的动态行为
- 为人工生命研究提供新范式，适合对生成式模拟感兴趣的学者
大型语言模型（LLMs）仅通过海量文本训练，便在胜景模式挑战（Winograd Schema Challenge, WSC）上取得优异表现，表明人类语言已高度刻画真实世界中的常识知识与推理能力。本研究尝试将语言的高表达力引入元胞自动机。具体而言，我们使用自然语言表示元胞状态与更新规则，并由大模型负责状态演算。该方法突破了传统元胞自动机仅限数值状态与固定规则的限制，构建出更丰富的仿真平台。本文提出LOGOS-CA（Language Oriented Grid Of Statements - Cellular Automaton）作为实现这一理念的自然框架，并验证其在简单森林火灾模拟中的有效性。同时，它也展现出作为人工生命（ALife）研究对象的独特潜力。本文报告实验结果，并探讨未来基于LOGOS-CA的研究方向。
原文摘要 · Abstract (English)
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.
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