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.
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