| > Werld drops 30 agents onto a graph with NEAT neural networks that evolve their own topology, 64 sensory channels, continuous motor effectors, and 29 heritable genome traits. communication bandwidth, memory decay, aggression vs cooperation — all evolvable. No hardcoded behaviours, no reward functions. - they could evolve in any direction. In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. "What are you doing?", asked Minsky. "I am training a randomly wired neural net to play Tic-tac-toe", Sussman replied. "Why is the net wired randomly?", asked Minsky. "I do not want it to have any preconceptions of how to play", Sussman said. Minsky then shut his eyes. "Why do you close your eyes?" Sussman asked his teacher. "So that the room will be empty." At that moment, Sussman was enlightened. |
Werld's room has walls. The graph topology, energy mechanics, metabolic costs, seasons, those are all design choices. But those are the physics, not the behavior. I chose the laws of nature, not what agents do with them.
Whether they cooperate or attack, broadcast or stay silent, grow complex brains or prune them down, that's selection, not me.
The agents also aren't randomly wired like Sussman's net — they start with minimal NEAT networks and evolve structure through survival. So the preconceptions are there, I just tried to make them physics rather than policy.
Curious how you would approach removing those from an artificial sim like this?