Controlling the Learning Dynamics of Interacting Self-Adapting Systems

Complex technical systems like robots or cars are composed of many embedded subsystems to control partial dynamical effects of the whole system. In order to ease engineering and to cope with changing environmental or system properties, these subsystems need to be self-adapting. But for this to be feasible, they cannot observe the theoretically required state space of the whole system. Instead, they need to work with a reduced set of input variables. This leads to a lack of information which may induce unintended, dynamic interactions between the self-adaptation processes. Within this paper, a method is proposed in order to control the self-adaptation processes and to fight these interactions in a goal directed way. The approach is investigated on a real robotic arm.