{"ID":2921010,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-04T07:41:34.29888543Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01976","arxiv_id":"2606.01976","title":"AutoBG: A Board Game Design Assistant with Interactive Ideation, Iterative Rulebook Generation, and Individualized Feedback","abstract":"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.","short_abstract":"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 throug...","url_abs":"https://arxiv.org/abs/2606.01976","url_pdf":"https://arxiv.org/pdf/2606.01976v1","authors":"[\"Zizhen Li\",\"Chuanhao Li\",\"Yibin Wang\",\"Jianwen Sun\",\"Yukang Feng\",\"Fanrui Zhang\",\"Mingzhu Sun\",\"Yifei Huang\",\"Kaipeng Zhang\"]","published":"2026-06-01T09:37:51Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}