How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles
This work addresses the challenge of creating open-ended evolutionary systems for AI and computational biology, though it appears incremental as it builds on existing swarm simulation concepts.
The paper tackles the problem of achieving open-ended evolution in artificial systems by simulating swarm dynamics, demonstrating that emergent structures can filter information through memory bottlenecks to produce meaningful novelty and diversity in three distinct applications.
We propose an approach of open-ended evolution via the simulation of swarm dynamics. In nature, swarms possess remarkable properties, which allow many organisms, from swarming bacteria to ants and flocking birds, to form higher-order structures that enhance their behavior as a group. Swarm simulations highlight three important factors to create novelty and diversity: (a) communication generates combinatorial cooperative dynamics, (b) concurrency allows for separation of timescales, and (c) complexity and size increases push the system towards transitions in innovation. We illustrate these three components in a model computing the continuous evolution of a swarm of agents. The results, divided in three distinct applications, show how emergent structures are capable of filtering information through the bottleneck of their memory, to produce meaningful novelty and diversity within their simulated environment.