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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Batory D. 2004. Program Comprehension in Generative Programming: A History of Grand
Challenges. Proceedings of 12th International Workshop on Program Comprehension.
Cover. T. M. and Thomas J. A. (1991). Elements of Information Theory. John Wiley & Sons Inc.
De Jong K. A. (2006). Evolutionary Computation - A Unified Approach. MIT Press pp. 26 109.
Goertzel B. (2006). The Hidden Pattern: A Patternist Philosophy of Mind. BrownWalker Press.
Goertzel B. (2007). Virtual Easter Egg Hunting. Proceedings of the AGI Workshop 2006. IOS
Press. p. 217.
Goertzel B. (2011). Itamar Arel on the Path to Artificial General Intelligence. Available
electronically at http://hplusmagazine.com/2011/02/04/itamar-arel-on-the-path-to-artificialgeneral-
Hawkins J.; and George D. (2006). Hierarchical temporal memory: concepts theory and
terminology. Available electronically at http://www.numenta.com/htmoverview/
Heljakka A.; Goertzel B.; Silva W.; Goertzel I.; and Pennachin C. (2007). Reinforcement
Learning of Simple Behaviors in a Simulation World Using Probabilistic Logic. In Advances in
Artificial General Intelligence IOS Press.
Koza J. R. 1992. Genetic Programming. MIT Press p. 79.
Oudeyer P.-Y. and Kaplan F. (2008). How can we define intrinsic motivation? Proceedings of
the 8th International Conference on Epigenetic Robotic.
Schaul T. and Schmidhuber J. (2010). Towards Practical Universal Search. Proceedings of
Artificial General Intelligence 2010. Atlantis Press.
Schmidhuber J. (2006). Godel Machines: Self-Referential Universal Problem Solvers Making
Provably Optimal Self-Improvements. Available electronically at
Schmidhuber J. (2004). Optimal Ordered Problem Solver. Machine Learning 54 211–254.
Kluwer Academic Publishers.
Skaba W. (2011). Heuristic Search in Program Space for the AGINAO Cognitive Architecture.
AGI 2011 Self-Programming Workshop. Available electronically at
Skaba W. (2012). Binary Space Partitioning as Intrinsic Reward. Proceedings of Artificial
General Intelligence 2012. LNAI 7716 Springer-Verlag.
Sutton R. S. and Barto A. G. (1998). Reinforcement Learning. MIT Press.