用大模型自动设计新卡牌游戏，省去人工反复试错。
Cardiverse: Harnessing LLMs for Novel Card Game Prototyping
- 用图结构索引生成新颖卡牌组合，突破已有数据库限制。
- 通过游戏记录验证代码一致性，确保规则稳定运行。
- 自研智能体靠大模型启发式函数对战，支持大规模测试。
卡牌游戏原型开发需大量人力进行创意构思与玩法评估。尽管大语言模型（LLMs）为自动化带来机遇，但其在设计超越现有数据库的新游戏机制、生成一致的游戏环境以及构建可扩展的玩法智能体方面仍面临挑战。本文提出一个全自动卡牌游戏原型框架：采用基于图的索引方法生成新颖游戏变体；利用LLM驱动系统生成一致的游戏代码，并通过玩法记录验证；设计一种集成多个LLM生成启发式函数的智能体，通过自对弈优化。该框架旨在加速原型开发，减少人力投入，降低开发者门槛。代码仓库见 https://github.com/danruili/Cardiverse。
原文摘要 · Abstract (English)
The prototyping of computer games, particularly card games, requires extensive human effort in creative ideation and gameplay evaluation. Recent advances in Large Language Models (LLMs) offer opportunities to automate and streamline these processes. However, it remains challenging for LLMs to design novel game mechanics beyond existing databases, generate consistent gameplay environments, and develop scalable gameplay AI for large-scale evaluations. This paper addresses these challenges by introducing a comprehensive automated card game prototyping framework. The approach highlights a graph-based indexing method for generating novel game variations, an LLM-driven system for consistent game code generation validated by gameplay records, and a gameplay AI constructing method that uses an ensemble of LLM-generated heuristic functions optimized through self-play. These contributions aim to accelerate card game prototyping, reduce human labor, and lower barriers to entry for game developers. For code repo visit this http URL https://github.com/danruili/Cardiverse
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