Dynamical Properties of Artificially Evolved Boolean Network Robots

In this work we investigate the dynamical properties of the Boolean networks (BN) that control a robot performing a composite task. Initially, the robot must perform phototaxis, i.e. move towards a light source located in the environment; upon perceiving a sharp sound, the robot must switch to antiphototaxis, i.e. move away from the light source. The network controlling the robot is subject to an adaptive walk and the process is subdivided in two sequential phases: in the first phase, the learning feedback is an evaluation of the robot’s performance in achieving only phototaxis; in the second phase, the learning feedback is composed of a performance measure accounting for both phototaxis and antiphototaxis. In this way, it is possible to study the properties of the evolution of the robot when its behaviour is adapted to a new operational requirement. We analyse the trajectories followed by the BNs in the state space and find that the best performing BNs (i.e. those able to maintaining the previous learned behaviour while adapting to the new task) are characterised by generalisation capabilities and the emergence of simple behaviours that are dynamically combined to attain the global task. In addition, we also observe a further remarkable property: the complexity of the best performing BNs increases during evolution. This result may provide useful indications for improving the automatic design of robot controllers and it may also help shed light on the relation and interplay among robustness, evolvability and complexity in evolving systems.