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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 et al.: Evolving the Rules of Play Themselves — Fukai Reads MORTAR
A paper on automatic game design by Nasir, Togelius and colleagues. Instead of levels, MORTAR evolves game mechanics themselves using a quality-diversity algorithm paired with a large language model, judging quality by whether stronger AI agents reliably beat weaker ones. Running on GPT-4o-mini, it generates diverse, playable games and even quantifies each mechanic's contribution.
