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Abstract
This article presents a microscopic model (agent positions, directions and interactions are explicitly modelled) of mobile agents (or self-propelled particles) that is inspired by the ‘complex transport networks’ reported by Jones (Citation2010; The emergence and dynamical evolution of complex transport networks from simple low-level behaviours, International Journal of Unconventional Computing 6, pp. 125–144). Here, the agents' positions are modelled continuously. This multi-agent system (or artificial swarm) shows a wide variety of self-organized pattern formations. The self-organization is based on the non-linearity of the agents' turns (discrete jumps in the agents' directions) and the indirect interactions of the agents via a potential field that determines their motion (high values are attractive) and which is changed by themselves (agents increase the value of the potential field at their positions). At least most of the irregular and complex patterns are transient. The patterns found during the transient are more complex than those the system converges to. Still, this transient behaviour is relevant. We empirically investigate the transient times in dependence of several system parameters and give examples.