The AGINAO Self-Programming Engine

TL;DR

The paper presents AGINAO, a self-programming cognitive engine for a humanoid robot that develops its control system through interaction with a natural environment, aiming for human-level artificial general intelligence.

Contribution

It introduces a novel self-programming approach for creating a cognitive engine that evolves through real-world interaction, moving beyond hand-crafted AI architectures.

Findings

The cognitive engine self-creates subroutines reflecting real-world patterns.

The overall program structure captures spatial and temporal dependencies.

The system demonstrates autonomous development of a hierarchical control program.

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.