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A Basic Architecture of an Autonomous Adaptive System With Conscious-Like Function for a Humanoid Robot.

Yasuo Kinouchi1, Kenneth James Mackin1

  • 1Department of Informatics, Tokyo University of Information Sciences, Chiba, Japan.

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Summary

This study proposes a brain-inspired AI control system architecture for humanoid robots. It introduces a "consciousness" function enabling autonomous adaptation, habitual, and goal-directed behaviors for enhanced robotic capabilities.

Keywords:
Libet’s experimentautonomous adaptationbinding problembrain-oriented systemgoal-directed behaviorhabitual behaviorimage processingmodel of consciousness

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Developing humanoid robots requires sophisticated control systems mimicking human cognition.
  • Existing systems often lack autonomous adaptation and integrated consciousness functions.
  • Understanding brain mechanisms is key to advancing artificial general intelligence.

Purpose of the Study:

  • To propose a novel brain-oriented control system architecture for humanoid robots.
  • To implement a functional definition of
  • consciousness
  • enabling both habitual and goal-directed behaviors.
  • To explain cognitive phenomena like the binding problem and Libet's experiment delay within the proposed framework.

Main Methods:

  • Designed a two-level artificial neural network architecture: a basic-system for consciousness and habitual behavior, and an extended-system for goal-directed behavior.
  • Modeled consciousness as a system-level adaptation function based on parallel-processing unit integration.
  • Integrated goal setting as an internal image for efficient action selection in goal-directed processes.

Main Results:

  • The proposed architecture autonomously adapts to environmental stimuli.
  • It successfully integrates habitual and goal-directed behaviors through a defined consciousness function.
  • The model offers explanations for the binding problem and Libet's experiment, linking awareness to decision-making.

Conclusions:

  • The brain-oriented architecture provides a framework for developing more adaptive and intelligent humanoid robots.
  • The functional definition of consciousness contributes to understanding AI and cognitive processes.
  • The two-level system design facilitates consistent and efficient robotic behavior through neural network interactions.