Evaluating Actuators in a Purely Information-Theory Based Reward Model
  AGINAO builds its cognitive engine by applying self-programming techniques to
create a hierarchy of interconnected codelets - the tiny pieces of code
executed on a virtual machine. These basic processing units are evaluated for
their applicability and fitness with a notion of reward calculated from
self-information gain of binary partitioning of the codelet's input
state-space. This approach, however, is useless for the evaluation of
actuators. Instead, a model is proposed in which actuators are evaluated by
measuring the impact that an activation of an effector, and consequently the
feedback from the robot sensors, has on average reward received by the
processing units.
