We present SOS++,
a bioinspired method combining
evolution
and learning,
allowing
the automatic design of the controller of autonomous agents,
described as a finite-state machine.
The
application of this method to well-known problems, for example the
follow-up of a trail or the resolution of a maze, led to the
emergence of some behaviors we could qualify as intelligent.
Moreover, it is possible to use the method in a hierarchical way in
order to obtain complex behaviors starting from a set of basic
actions. We have used an algorithm which is a variation of
reinforcement learning with a reward adapted to the degree of
uncertainty of the performed prediction.