In complex systems modelling, two kinds of features are to be considered. On the one hand, non-predicted and complex real worlds need a distribution of the complexity in atomic pieces to better understand, control, and isolate underlying phenomena. On the other hand, agents could not model their surroundings in details, as there are too many unknown events to consider. It is then not surprising that neither purely reactive nor purely cognitive architectures can be flexible enough for complex systems modelling. This fact is widely accepted in many areas, related in Fergusson (1994) [2] and Wooldridge and Jennings (1994) [10] for instance. The approach proposed in GEAMAS is to marry cognitive and reactive architectures. Such an architecture is called hybrid, according to the common definition found in Wooldridge and Jennings (1994) [10].
The architecture of GEAMAS describes an abstracted model of the real world.
This foundation is inherent to the problem to be solved, namely modelling and
distributing complexity. To find the adequate separation, GEAMAS is based on
the distribution of roles: macro-agents are related elements in which emergent
behaviours will be observed, and describe agents in an organised society;
interactions, micro-behaviours and evolution facilities of the agent are embedded
into cognitive and reactive agents.