Abstract

This chapter has the objective of describing the structure and placing in a taxonomy the Artificial Adaptive Systems (AAS). These systems form part of the vast world of Artificial Intelligence (AI) nowadays called more properly Artificial Sciences (AS). Artificial Sciences means those sciences for which an understanding of natural and/or cultural processes is achieved by the recreation of those processes through automatic models. In particular, Natural Computation tries to construct automatic models of complex processes, using the local interaction of elementary micro-processes, simulating the original process functioning. Such models organize themselves in space and time and connect in a non-linear way to the global process they are part of, trying to reproduce the complexity through the dynamic creation of specific and independent local rules that transform themselves in relation to the dynamics of the process. Natural Computation constitutes the alternative to Classical Computation (CC). This one, in fact, has great difficulty in facing natural/cultural processes, especially when it tries to impose external rules to understand and reproduce them, trying to formalize these processes in an artificial model. In Natural Computation ambit, Artificial Adaptive Systems are theories which generative algebras are able to create artificial models simulating natural phenomenon. The learning and growing process of the models is isomorphic to the natural process evolution, that is, it’s itself an artificial model comparable with the origin of the natural process. We are dealing with theories adopting the “time of development” of the model as a formal model of “time of process” itself. Artificial Adaptive Systems comprise Evolutive Systems and Learning Systems. Artificial Neural Networks are the more diffused and best-known Learning Systems models in Natural Computation.