Artificial collectives of specialists and generalists excel at different tasks

The study presents insights into collective artificial intelligence by examining how specialist and generalist agents perform in multi-agent systems. It reveals that collectives of specialists, characterized by sparse networks, excel in negotiation tasks, while generalists, with dense networks, are superior in generating and coordinating tasks. The research identifies a trade-off between performance and convergence speed based on rationality bounds, suggesting that optimizing agent interpretive networks according to task requirements and computational constraints can enhance the efficiency and energy costs of multi-agent systems, which is critical for practitioners designing these systems.