Evolution of Collective AI Beyond Individual Optimization
This research addresses the issue of collective AI performance degradation due to individual over-optimization, which is incremental as it builds on existing multi-agent simulation studies.
The study tackled the problem of how individual optimization affects collective behavior in homogeneous agents, finding that over-optimization led to reduced sensor-motor coupling and a significant decline in collective fitness despite high individual performance.
This study investigates collective behaviors that emerge from a group of homogeneous individuals optimized for a specific capability. We created a group of simple, identical neural network based agents modeled after chemotaxis-driven vehicles that follow pheromone trails and examined multi-agent simulations using clones of these evolved individuals. Our results show that the evolution of individuals led to population differentiation. Surprisingly, we observed that collective fitness significantly changed during later evolutionary stages, despite maintained high individual performance and simplified neural architectures. This decline occurred when agents developed reduced sensor-motor coupling, suggesting that over-optimization of individual agents almost always lead to less effective group behavior. Our research investigates how individual differentiation can evolve through what evolutionary pathways.