Behavioral feedback as a catalyst for emergence in multi-agent systems

Swarm algorithms rely on randomness to produce solutions for complex problems. The random selection of actions and chance interactions of agents force a swarm to attempt many behavioral possibilities - reinforcing the productive ones and dampening the dead ends. Randomness however, is a dual-edged sword: it is necessary to insure a wide range of agent behavior, but also a source of inefficiency and wasted resources. Using behavioral feedback, we reinforce effective use of randomness - using it to select from a restricted list of useful actions. By observing an agent's behavior over the three domains of time, space, or category, we establish a context for the application of randomness. The set of possible agent actions can be reduced to only those that are potentially beneficial. With this constraint, our results show we can dramatically improve performance and induce faster emergence from swarm algorithms using behavioral feedback