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, and distributed controls can enable
self-reconfigurable agents to performance locomotion and
reconfiguration. The DHM is an integration and generalization of
reaction-diffusion model and stochastic cellular
automata
. The movements of agents (or
cells) in DHM are computed not by the Turing's differential
equations, nor the Metropolis rule
, 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. Furthermore, an extension of this model may be directly
applicable to self-organization in multi-agent
systems
using simulated hormone-like signals.