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In this paper we present a strategy for inducing a behavior in a real agent through a learning process with a human teacher. The agent creates internal models extracting information from the consequences of the actions it must carry out, and not just learning the task itself. The mechanism that permits this background learning process is the Multilevel Darwinist Brain, a cognitive mechanism that allows an autonomous agent to decide the actions it must apply in its environment in order to fulfill its motivations. It is a reinforcement based mechanism that uses evolutionary techniques to perform the on line learning of the models. [ABSTRACT FROM AUTHOR]

In this work, we will use self-organizing feature maps as a method of visualization the sampling of the fitness space considered by the populations of two evolutionary methods, genetic and macroevolutionary algorithms, in a case with a mostly flat fitness landscape and low populations. Macroevolutionary algorithms will allow obtaining better results due to the way in which they handle the exploration-exploitation equilibrium. We test it with different alternatives using the self-organizing maps. [ABSTRACT FROM AUTHOR]

Combining previous experience and knowledge to contemplate tasks of increasing complexity is one of the most interesting problems in autonomous robotics. Here we present an ANN based modular architecture that uses the concept of modulation to increase the possibilities of reusing previously obtained modules. A first approximation to the modulation of the actuators was tested in a previous paper where we showed how it was useful to obtain more complex behaviours that those obtained using only activation / inhibition. In this paper we extend the concept to sensor modulation, which enables the architecture to easily modify the required behaviour for a module, we show how both types of modulation can be used at the same time and how the activation / inhibition can be seen as a particular case of modulation. Some examples in a real robot illustrate the capabilities of the whole architecture. [ABSTRACT FROM AUTHOR]

This work deals with the problem of automatically obtaining ANNs that cooperate in modelling of complex functions. We propose an algorithm where the combination of networks takes place at the phenotypic operational level. Thus, we evolve a population of networks that are automatically classified into different species depending on the performance of their phenotype, and individuals of each species cooperate forming a group to obtain a complex output. The components that make up the groups are basic ANNs (primitives) and could be reused in other search processes as seeds or could be combined to generate new solutions. The magnitude that reflects the difference between ANNs is their affinity vector, which must be automatically created and modified. The main objective of this approach is to model complex functions such as environment models in robotics or multidimensional signals. [ABSTRACT FROM AUTHOR]