**9. Conclusions**

By coupling two self-activated n-flops, we end up with an autonomous behavior initiator system that mimics the functioning of a living brain, in the sense that a default network consumes energy and is ready to initiate other behaviors under specific stimulus. Due to n-flops activity, all behaviors are constantly self-pushed from the inside.

With behaviors pushing from the inside, the robot is quite ready to face the real word and quickly learn new tricks. This is corroborated by the relative small number of mutation required to evolve reliable robots.

Our model incorporates some basic aspect of biological brains: (a) a fraction of the overall activity of all energy used by the autonomous neural controller (ANC) occurs in circuits unrelated to any external event. (b) In terms of structure, the components of the ANC are separated, carefully knitted constructions with pronounced job specializations.

Complex behaviors are codified in one single chromosome with 198 genes.

This satisfies one of the basic rules of evolution: Few genetic information unravels into complex things.

It seems reasonable to conclude that in a compact gene, small mutations produce enormous changes in the mutated individual, which enriches the search for solutions.

At least for our model of ANC, a successfully interaction with humans depends on the human attitude, if the humans put too much emphasis on the robot to learn to stay quiet. On the other hand, if human stays quiet but the basic rules of the game (lift the ball) is passed on to the robot learning, then the robot will pick up to the hard part of the job.

As in biology our robots, concerning behavior initiation, do throw the dice, but they keep and attractively control over when, where, and how this random event will be put into effect.
