How do neural networks evolve for complex agent behavior?
Evolving Neural Networks Reveal Emergent Collective Behavior from Minimal Agent Interactions
October 28, 2024
https://arxiv.org/pdf/2410.19718This research investigates how simple, localized interactions between agents powered by small, evolvable neural networks can lead to complex, emergent collective behaviors, similar to flocks of birds or schools of fish.
Key findings relevant to LLM-based multi-agent systems:
- Emergent Complexity: Even with minimal intelligence (small neural networks, local interactions), agents can self-organize into complex patterns.
- Network Non-Linearity: The complexity of the emergent behavior correlates with the non-linearity of the agents' neural networks. More sophisticated behaviors require more complex processing.
- Parameter Impact: Environmental factors (e.g., noise, agent density, "vision" range) significantly influence both the emergent patterns and the neural networks themselves.
- Potential for LLMs: This suggests that similar principles could be applied to LLM-based agents. By carefully tuning environmental parameters and agent interactions, we could potentially guide LLMs towards more complex and useful forms of collaboration.