  The AGINAO is a project to create a human-level artificial general
intelligence system (HL AGI) embodied in the Aldebaran Robotics' NAO humanoid
robot. The dynamical and open-ended cognitive engine of the robot is
represented by an embedded and multi-threaded control program, that is
self-crafted rather than hand-crafted, and is executed on a simulated Universal
Turing Machine (UTM). The actual structure of the cognitive engine emerges as a
result of placing the robot in a natural preschool-like environment and running
a core start-up system that executes self-programming of the cognitive layer on
top of the core layer. The data from the robot's sensory devices supplies the
training samples for the machine learning methods, while the commands sent to
actuators enable testing hypotheses and getting a feedback. The individual
self-created subroutines are supposed to reflect the patterns and concepts of
the real world, while the overall program structure reflects the spatial and
temporal hierarchy of the world dependencies. This paper focuses on the details
of the self-programming approach, limiting the discussion of the applied
cognitive architecture to a necessary minimum.
