Collective Complexity out of Individual Simplicity

Abstract

The concept of Swarm Intelligence (SI) was first introduced by Gerardo Beni, Suzanne Hackwood, and Jing Wang in 1989 when they were investigating the properties of simulated, self-organizing agents in the framework of cellular robotic systems [1]. Eric Bonabeau, Marco Dorigo, and Guy Theraulaz extend the restrictive context of this early work to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies,” such as ants, termites, bees, wasps, “and other animal societies.” The abilities of such systems appear to transcend the abilities of the constituent individuals. In most biological cases studied so far, robust and capable high-level group behavior has been found to be mediated by nothing more than a small set of simple low-level interactions between individuals, and between individuals and the environment. The SI approach, therefore, emphasizes parallelism, distributedness, and exploitation of direct (agent-to-agent) or indirect (via the environment) local interactions among relatively simple agents.