用GUI智能体持续测试生成游戏,提升可玩性。
GUI Agents for Continual Game Generation

- 用GUI智能体自动运行并评估游戏,替代人工试玩。
- 新方法使游戏可玩性评分达66.8%,超越基线37.1点。
- 适合研究交互式代码生成与自动化游戏测试的人。
生成游戏不等于生成可玩的游戏。尽管代码生成技术进步,现有方法仍将其视为从提示到成品的一次性转换,无法发现交互层面的缺陷。我们提出让图形用户界面(GUI)智能体扮演双重角色:一是客观评估者,引入PlaytestArena环境,将200个基于浏览器的游戏生成任务(涵盖八种类型)与预期行为标准配对,由GUI智能体加载并运行每个版本进行测试;二是主观体验者,提出Play2Code框架,让游戏智能体与GUI智能体在共享内存中持续互动,实现编码与试玩的对话式迭代。实验表明,即使前沿模型也难以直接生成可玩游戏,而Play2Code达到66.8%的评测通过率,相比单次生成和代理式编码基线分别提升37.1和14.6个百分点。进一步分析显示,GUI反馈比人类报告更可追踪,但也有类似人类的个体差异,确立游戏试玩作为交互式代码生成的关键验证场景。
原文摘要 · Abstract (English)
Generating a game is not the same as making one that can be played. Despite advances in code generation, existing approaches treat game generation as one-shot translation from prompt to artifact, leaving interaction-level failures undetected. We argue that evaluating and improving game generation requires a player, and study two roles for graphical user interface (GUI) agents in this process: (1) as an objective evaluator, for which we introduce PlaytestArena, a new evaluation environment that pairs 200 browser-based game generation tasks across eight genres with rubrics of expected in-play behaviors, adjudicated by a GUI agent that loads each build in a browser and plays it; and (2) as a subjective playtester, for which we propose Play2Code, where a game agent and a GUI agent operate in a sustained loop with shared memory, turning game generation into a dialogue between coding and playing. Our experiments show that even frontier models struggle to generate playable games directly, while Play2Code achieves a 66.8\% rubric pass-rate, improving over single-pass and agentic-coding baselines by 37.1 and 14.6 points respectively. Further analysis shows that GUI playtester feedback is more traceable than a human report, yet idiosyncratic in ways reminiscent of human testers, establishing game playtesting as a critical testbed for interactive code generation. Our project website is available at https://continual-game-generation.vercel.app/.
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