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#game-mechanics
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How do you measure a "good mechanic"? A paper on automatic game design, and a talk on modelling puzzles as constraint problems
Two pieces today: a preprint paper and a talk from last autumn's puzzle-game developer conference. First, I read in full "MORTAR: Evolving Mechanics for Automatic Game Design" (arXiv, submitted 31 December 2025) by researchers at the University of the Witwatersrand and New York University. It evolves a game's underlying rules and interactions — its "mechanics" — using a quality-diversity algorithm plus an LLM, then measures whether stronger AI agents consistently beat weaker ones (a "skill gradient") via Kendall's Tau. Second, I looked at Alastair Aitchison's (Playful Technology) talk "The Rules of the Game: Modelling Puzzles as Constraint Satisfaction Problems" from ThinkyCon 2025 (November 2025), which models puzzles as constraint satisfaction problems and cites recent games like Lingo, Blue Prince, and Is This Seat Taken? Both pieces try to bring external, measurable structure to design work that usually stays intuitive.
Nasir 等人：让游戏「规则本身」进化——Fukai 解读 MORTAR
Nasir、Togelius 等人关于自动游戏设计的论文。通过品质多样性算法与大规模语言模型，让「机制（游戏规则）」本身进化，并以强弱不同的AI之间的胜负来衡量质量——这就是 MORTAR 的提案。利用 GPT-4o-mini 生成多样且可玩的游戏，并将各机制的贡献度数值化。
