SWARM INTELLIGENCE SYSTEMS USING GUIDED SELF-ORGANIZATION FOR COLLECTIVE PROBLEM SOLVING

Abstract

Drawing inspiration from social interactions in nature, the field of swarm intelligence has presented a promising approach to the design of complex systems consisting of numerous, usually homogeneous, simple parts, to solve a wide variety of problems. Like cellular automata, swarm-intelligence systems involve highly parallel computations across space, based heavily on self-organization, the emergence of global behavior through local interactions of components, and the absence of centralized or global control. However, this has a disadvantage as the desired behavior of a system becomes hard to predict or design based on its local interaction rules. In our ongoing research, we propose to provide greater control over a system, and potentially more useful, goal-oriented behavior, by introducing layered, hierarchical controllers in the particles or components. The layered controllers allow each particle to extend their reactive behavior in a more goal-oriented style, while keeping the locality of the interactions and the general simplicity of the system. In this paper, we present three systems designed using this approach: a competitive foraging system, a system for the collective transport and distribution of goods, and a self-assembly system capable of creating complex structures in a 3D world. Our simulation results show that in all three cases it was possible to guide the self-organization process at different levels of the designated task, suggesting that the self-organizing behavior of swarm-intelligence systems may be extendable to support problem solving in various contexts, such as coordinated robotic teams.