用AI自动设计游戏机制，让强玩家始终胜过弱玩家。
Mortar: Evolving Mechanics for Automatic Game Design
- 结合语言模型与多样性算法，自动演化游戏规则
- 生成的游戏能保持强弱玩家的技能区分度
- 适合游戏设计自动化与创意生成研究者
我们提出Mortar，一个用于自动游戏设计中演化游戏机制的系统。游戏机制定义了玩法规则与交互逻辑，手动设计耗时且依赖专家经验。Mortar将质量-多样性算法与大语言模型结合，探索多样化的机制组合，并通过树搜索合成完整游戏，评估其是否维持玩家间的技能排序——即强玩家是否持续胜过弱玩家。机制表现基于其对游戏技能排序得分的贡献进行评价。实验表明，Mortar生成的游戏具有多样性和可玩性，且所产生机制显著提升技能排序表现。通过消融实验验证各组件作用，并开展用户研究，结果支持其有效性。
原文摘要 · Abstract (English)
We present Mortar, a system for autonomously evolving game mechanics for automatic game design. Game mechanics define the rules and interactions that govern gameplay, and designing them manually is a time-consuming and expert-driven process. Mortar combines a quality-diversity algorithm with a large language model to explore a diverse set of mechanics, which are evaluated by synthesising complete games that incorporate both evolved mechanics and those drawn from an archive. The mechanics are evaluated by composing complete games through a tree search procedure, where the resulting games are evaluated by their ability to preserve a skill-based ordering over players -- that is, whether stronger players consistently outperform weaker ones. We assess the mechanics based on their contribution towards the skill-based ordering score in the game. We demonstrate that Mortar produces games that appear diverse and playable, and mechanics that contribute more towards the skill-based ordering score in the game. We perform ablation studies to assess the role of each system component and a user study to evaluate the games based on human feedback.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。