LOGOS-CA: A Cellular Automaton Using Natural Language as State and Rule

TL;DR

LOGOS-CA introduces a novel cellular automaton framework that uses natural language for cell states and rules, leveraging LLMs to enable richer, more flexible simulations beyond traditional numerical automata.

Contribution

This paper presents LOGOS-CA, a cellular automaton that employs natural language and LLMs for states and rules, expanding the capabilities of traditional automata.

Findings

Successfully simulated forest fire scenarios

Demonstrated potential for artificial life research

Showed flexibility of language-based automata

Abstract

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.