The AGINAO Self-Programming Engine

W. Skaba
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引用次数: 3

Abstract

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.
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AGINAO自编程引擎
AGINAO是Aldebaran Robotics公司的NAO类人机器人,旨在创建一个人类水平的人工通用智能系统(HL AGI)。机器人的动态开放式认知引擎由一个嵌入式多线程控制程序表示,该程序是自己制作的,而不是手工制作的,并在模拟通用图灵机(UTM)上执行。认知引擎的实际结构是将机器人置于自然的学前环境中,并运行一个核心启动系统,该系统在核心层之上执行认知层的自我编程。来自机器人感官设备的数据为机器学习方法提供了训练样本,而发送给执行器的命令则可以测试假设并获得反馈。每个自创建的子例程应该反映现实世界的模式和概念,而整个程序结构反映世界依赖关系的时空层次结构。本文主要关注自编程方法的细节,将应用认知体系结构的讨论限制在必要的最低限度。
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