- Stop**. This behavior is evolved to minimize the speed of the center of mass of**
the robot. Note that a very small number of generations is needed **to get the**
perfect behavior, but that all evolved Stop **behaviors in fact rotate rapidly**
with inverse speeds on both motors.

- Area sweeping**. The arena is divided in small squares, and the fitness is the**
number of squares that were visited by the robot during 10 epoch 4.
Two types of supervisors have been tested: the Classical Supervisor (CS), using Classical Controllers as basic behaviors, and the Symbolic Supervisor (SS), that uses symbolic controllers (see section 3) as basic behaviors. The NN implementing the supervision are are Elman networks 5 **with 5 hidden neurons.**
Baseline experiments were also performed using direct controllers **with the**
same Elman architecture - either Classical, or Symbolic (see section 3).

All supervisors and direct controllers have 17 inputs: the 8 IR sensors in active mode for obstacle detection, the 8 IR sensors in passive mode for ambient light detection, and the accumulator level.

The number of outputs is 2 for the classical controller, the speeds **of the motors, 6 for the symbolic controller using the three hard-coded behaviors** FORWARD, LEFT, RIGHT **(see section 3), and 4 for both supervisors (Classical and**
Symbolic) that use the evolved basic behaviors obstacle avoidance, light following, area sweeping and stop**. The** obstacle avoidance **behaviors that are used are**
the best results obtained in the experiments of section 5. Similar experiments
(i.e. with the same architectures) were run for the area sweeper **and the best** results of 10 runs were chosen. For the simpler light following and stop**, 2-layers**
networks were sufficient to get a perfect fitness.

![0_image_0.png](0_image_0.png)

