How does agent diversity boost collective AI learning?
Neural diversity is key to collective artificial learning
This research demonstrates that behavioral diversity among AI agents is crucial for effective collective learning in multi-agent systems. Diverse agents outperform homogeneous (identical) agents in cooperative tasks like simulated soccer, exploration tasks like foraging, and resilience tasks involving adapting to environmental changes. Diversity leads to the emergence of specialized roles (like goalkeeper) and more complex strategies, even without explicit instructions.
For LLM-based multi-agent systems, this research highlights the importance of designing for agent heterogeneity. Rather than building multiple copies of the same LLM agent, encouraging diverse behaviors and specializations might be key to achieving complex goals. This could involve training with diverse prompts, varying the LLMs' architectures or fine-tuning data, or incorporating explicit diversity objectives into the training process. This work also offers tools for measuring and controlling diversity within agent teams which could be applicable to LLM agents.