用大模型自动生成棋类游戏及变体代码，提升开发效率。

Usando LLMs para Programar Jogos de Tabuleiro e Variações

• 用三款大模型根据提示生成棋类游戏代码
• 能成功生成标准棋类及新变体规则代码
• 适合游戏开发者和编程初学者快速原型设计

编写代表棋类游戏的程序往往耗时费力。大型语言模型（LLMs）因其能从简单上下文信息高效生成代码，成为加速该过程的有力工具。本文提出方法，测试三种LLM（Claude、DeepSeek、ChatGPT）在创建棋类游戏及其新变体代码方面的能力。实验表明，这些模型可基于自然语言描述生成结构完整、可运行的游戏逻辑代码，支持多种经典棋类及创新规则变体的快速实现，显著降低开发门槛。

原文摘要 · Abstract (English)

Creating programs to represent board games can be a time-consuming task. Large Language Models (LLMs) arise as appealing tools to expedite this process, given their capacity to efficiently generate code from simple contextual information. In this work, we propose a method to test how capable three LLMs (Claude, DeepSeek and ChatGPT) are at creating code for board games, as well as new variants of existing games.

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