If a method doesn't work, don't patch it—discard it and carry the "XP" (the lesson) into the next attempt.
The program treated any problem—from city traffic to supply chain breakdowns—as a procedurally generated dungeon. File: AI.Roguelike.zip ...
Unlike standard AI, which learns from static past data, this "Roguelike" AI used . If a simulation failed to reach the goal, the entire branch of logic was deleted instantly. Only the most "hardened" code survived to the next floor. 2. The Use Case: Saving a Small Town If a method doesn't work, don't patch it—discard
Elias tested the file on a local crisis: a failing food bank distribution network during a record blizzard. If a simulation failed to reach the goal,
The AI didn't just map routes; it "played" the storm. It sacrificed non-perishable delivery speed (taking a "hit" to health) to ensure insulin and fresh milk reached the highest-risk neighborhoods (the "boss room").
By treating real-world logistics as a high-stakes game, the program found a 14% more efficient route than human dispatchers, simply because it wasn't afraid to "lose" a simulation to find the winning path. 3. The Useful Takeaway