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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.
Can AI Build a Whole Puzzle Game? ScriptDoctor and Its Generate-Playtest-Repair Loop
ScriptDoctor has a large language model write an entire puzzle game — rules, sprites, levels — then lets a compiler and a search-based agent inspect the result and demand revisions. The testbed is PuzzleScript, a language indie developers know well. I walk through the paper in five parts — problem, method, findings, where you can use it, limitations — covering why human-authored examples boost success rates, why reasoning models win, and the distance between 'solvable' and 'fun'.