whole, and the incorporation of all these properties in a single model provides great robustness
in the behaviour production. The result is a BPS with a very high degree of adaptation.
The bottom-up and evolutionary approach followed in the construction of our BPS
allows the increase of a given configuration of the BPS with the incorporation of new layers
over existing layers, while preserving the capabilities of the previous ones. The new
incorporated layers define types of behaviours that are more complex, which are required when
the problem to solve also becomes itself more complex. In this sense, we could think that when
the manipulation of concepts and logic is required in order to select behaviours, then cognitive
behaviours could be incorporated into the BPS as a new layer over the layer of motivated
behaviours, in the same way in which this last layer was incorporated over the layer of reactive
behaviours, when the motivations were taken into account for the action selection. Of course,
not only would the complexity of the BPS be increased, but we would need also to take into
account other issues, such as societies, language, and culture. Therefore, our BPS is capable to
be evolved when the problem to solve becomes more complex.
Our BPS is context-free, because it is independent of the motor and perceptual systems
of the artificial creature to be controlled. Since the perceptual and motor systems are
environment-dependent, our BPS can be easily used in different environments (robots, virtual
animats, software agents, etc.), by just designing the appropriate perceptual and motor systems
for the given environment of the artificial creature.
The two types of learning schemes present in the BPS, associative learning and dynamic
adjustment of motivation degree, were obtained through a refinement process of the previously
defined layers. Both types of learning have improved the behaviour production, doing it more
adaptive. That is to say, our BPS is characterized by adaptation by learning (Meyer and Guillot,
1990). The associative learning allows new behaviours and emergent properties to arise, which
increase the adaptive level of the BPS. The dynamic variation of parameter " in the model for
combination of external and internal stimuli (expression (11)) allows the autonomous agent to
contend with an environment from which the agent possesses certain knowledge, which is
summarized in the value of this parameter.
We can also say that BeCA presents emergent cognition, in a Turing style (Turing,
1950). This is, an observer of an artificial creature controlled by BeCA (e.g. animats in our
Behaviours Virtual Laboratory) may judge that the creature knows what he is doing. Our
intention was not that BeCA would provide cognition to an artificial creature, not even the
simple cognition that emerges for observers, but it does. Of course it is low cognition, present
in animal behaviour. But we believe that this cognition is also emergent in animals, and that
higher cognition should also be emergent. Cognition is not a mechanism. It is an exhibition of
capabilities. And this exhibition must be perceived by an observer in order to be considered as
cognition.
BeCA was implemented in the Behaviours Virtual Laboratory, to be presented in
Chapter 5, providing the behaviour production of animats. In the next section we will present
a simple model of social action, which allows complex social phenomena to emerge from the
interactions of agents.
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