Digital Hormone Models for Self-Organization

摘要

How do multiple elements/agents self-organize into global patterns based on local communications and interactions? This paper describes a theoretical and simulation model called "Digital Hormone Model" (DHM) for such a self-organization task. The model is inspired by two facts: complex biological patterns are results of self-organization of homogenous cells regulated by hormone-like chemical signals (Jiang et al. 1999), and distributed controls can enable self-reconfigurable agents to performance locomotion and reconfiguration (Shen, Salemi, & Will 2000; Shen, Lu, & Will 2000; Salemi, Shen, & Will 2001). The DHM is an integration and generalization of reaction-diffusion model (Turing 1952) and stochastic cellular automata (Lee et al. 1991). The movements of agents (or cells) in DHM are computed not by the Turing's differential equations, nor the Metropolis rule (Kirkpatrick & Sorkin 1995), but by stochastic rules that are based on the concentration of hormones in the neighboring space. Experimental results have shown that this model can produce results that match and predict the actual findings in the biological experiments of feather bud formation among uniform skin cells (Jiang et al. 1999). Furthermore, an extension of this model may be directly applicable to self-organization in multi-agent systems using simulated hormone-like signals.