Designing a board game demands both thinking as a designer and experiencing as a player, while iterating through repeated prototyping and playtesting cycles, making it a cognitively intensive creative task well suited for human-AI collaboration. However, current systems lack end-to-end support to guide designers through the complete workflow from vague early ideation to iterative rulebook revision and audience testing. To this end, we present AutoBG, a board game design assistant built around critic-driven iterative refinement, comprising four specialized modules: BG-Ideator guides designers via multi-turn dialogue to produce structured design drafts; BG-Realizer generates complete rulebooks from drafts and revises them in a closed loop with BG-Critic, which diagnoses design flaws and gates each revision so that only verified improvements are accepted; and BG-Persona simulates individualized feedback from 150 real player profiles. Together, these modules enable designers to go from an initial idea to a polished, audience-tested rulebook within a single integrated workflow. The system is built on 2.2K structured rulebooks and 180K quality-filtered real player reviews, with task-specific training data derived for each module. Experiments on 207 held-out games show that AutoBG substantially outperforms state-of-the-art baselines (e.g., GPT-5.4), generating rulebooks that approach the quality of published games. Furthermore, a user study with 30 participants across diverse experience levels confirms that AutoBG effectively reduces blank-page anxiety, surfaces hidden design flaws, and provides highly rated, practical assistance throughout the creative process.

翻译：摘要：设计一款桌面游戏既需要设计者的构思视角，又需要玩家的体验视角，同时还要经历反复的原型制作与试玩测试循环，这使得它成为一项认知密集型创作任务，非常适合人机协作。然而，现有系统缺乏端到端的支持，无法引导设计者完成从模糊的早期构思到迭代规则修正及受众测试的完整工作流程。为此，我们提出AutoBG——一款基于评判驱动迭代优化的桌游设计助手，由四个专用模块构成：BG-Ideator通过多轮对话引导设计者生成结构化设计草案；BG-Realizer从草案生成完整规则书，并与BG-Critic形成闭环修正——BG-Critic诊断设计缺陷并控制每次修订的准入，确保仅通过验证的改进被采纳；BG-Persona则模拟来自150份真实玩家档案的个性化反馈。这些模块协同工作，使设计者能在单一集成流程中，从初始构想直入经过打磨和受众测试的规则书。该系统基于2,200份结构化规则书与180,000条经过质量过滤的真实玩家评论构建，并为每个模块派生任务专用训练数据。在207款留存游戏上的实验表明，AutoBG显著优于最先进的基线（如GPT-5.4），生成的规则书质量接近已出版游戏。此外，一项涵盖30名不同经验水平参与者的用户研究证实，AutoBG能有效缓解白页焦虑、暴露隐藏设计缺陷，并在整个创作过程中提供高评价的实用辅助。