Through this work we could see, not only that science is understanding how animals
behave, preparing the road to understanding cognition; but also that we are able to simulate
this adaptive behaviour, building artificial systems with the same properties than the ones
present in animal behaviour.
Intelligent behaviour depends on the observer, and also the emergent low-level
cognition exhibited by our animats. The animats act as if they would know what they are doing.
Do they really know what they are doing? Do we really know what we are doing? If they
perform in the same way, the rest is just a matter of interpretation, not important for their
actions. The animats behave the way they do, independently of the names we put to the
mechanisms that produce their behaviours.
The significance of the Behavioural Columns Architecture lies in its ability to model and
simulate adaptive behaviour in such a complete way. The number and quality of the properties
presented in BeCA is the highest of all the BPSs and ASMs proposed to date. Also, since it was
designed in a bottom-up fashion, more functionality can be added to it in order to improve its
performance and capabilities. The fact that it is a context-free BPS, allows it to be implemented
in different environments, by designing only the perceptual and motor systems, which are
dependent of the environment. Also, the production of behaviours emerging from the
interactions among the elemental behaviours, each of which, by itself, is not important in the
behaviours production, is noticeable.
Our Imitation and Induction model for social action shows that complex social
behaviour might be described, and therefore, undestrood, with very simple rules. That the
complexity of the behaviour of the system is determined not only by the complexities of the
individuals, but also by the amount of individuals in the system, and the number of their
interactions.
Our Behaviours Virtual Laboratory is quite a useful tool. It allowed us to test and
validate BeCA and I&I, and now it is available for the community for understanding adaptive
and social behaviour, or even just for playing with it. Also, since the source code has been made
public for the community, programmers can adapt it and expand it. Students can learn
properties of adaptive behaviour, behaviour-based systems, artificial societies, complex systems,
object-oriented programming, and virtual reality with our BVL.
The experiments presented allowed us to exhibit the properties of BeCA and the I&I
model using our BVL, showing the capabilities of the BVL at the same time.
We are able to create artificial adaptive autonomous agents. This paves the road for
creating fully rational and conscious agents.
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