The AGINAO Self-Programming Engine

Abstract

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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 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- intelligence

  • Hawkins, J.; and George, D. (2006). Hierarchical temporal memory: concepts, theory, and terminology. Available electronically at http://www.numenta.com/htmoverview/ education/Numenta_HTM_Concepts.pdf

  • 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 http://www.idsia.ch/~juergen/gmweb3/gmweb3.html

  • 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 http://www.iiim.is/wp/wp-content/uploads/2011/05/skaba-agisp-2011.pdf

  • 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.

OPEN ACCESS

Journal + Issues

Search