or away from it). The robot must hence learn from the first epoch the state of that random variable, and act accordingly in the following epochs.

The controller architecture is designed manually in the original experience, whereas evolution has complete freedom in its recent remake [16]. Moreover, Tuci et al. use no explicit reinforcement. Nevertheless, the results obtained by this recent approach are much better than the original results - and the authors claim that the reason for that lies in their complete black-box approach.

However, whereas the designers did decide to use a specifically designed modular architecture in the first experiment, the second experience required a careful design of the fitness function (for instance, though the reward lies under the light only half of the time, going toward the light has to be rewarded more than fleeing away to break the symmetry). So be it at the "innate" or "acquired" level, human intervention is required, and must act at some very high level **of subtlety.**
Going beyond this virtual "innate/acquired" debate, an intermediate issue would be to be able to evolve complex controllers that could benefit from human knowledge but that would not require high level of intervention with respect to the complexity of the target task.

Such an approach is what is proposed here: the designer is supposed to help the evolution of complex controllers by simply seeding the process with some simple behaviors - hand-coded or evolved - letting evolution arrange those building blocks together. An important side-effect is that the designer will hopefully be able to better understand **the results of an experiment, because of the greater**
intelligibility of the controllers. It then becomes easier to manually optimize the experimental protocol, e.g. to gradually refine the fitness in order **to solve some** very complex problems.

## 3 Symbolic Controllers 3.1 Rationale

The proposed approach pertains to Evolutionary Robotics [13]. Its **originality** lies in the representation space of the controllers, i.e. the search space of the evolutionary process. One of the main goals is that the results will be **intelligible**
enough to allow an easy interpretation of the results, thus easing the whole design process.

A frequent approach in Evolutionary Robotics is to use Neural Networks as controllers (feedforward or recurrent, discrete or continuous). The inputs of the controllers are the sensors (Infra-red, camera, . . . ), plus eventually some reward "sensor", either direct [18] or indirect [16].

The outputs of the controllers are the actuators of the robot (e.g. speeds of the left and right motors for a Khepera robot). The resulting controller is hence comparable to a program in machine language, thus difficult to interpret. To overcome this difficulty, we propose to use higher level outputs, namely involving four possible actions: Forward, Right, Left and Backward**. In order to allow**
some flexibility, each one of these symbolic actions should be tunable by some