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Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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Self in NARS, an AGI System.

Pei Wang1, Xiang Li1, Patrick Hammer1

  • 1Department of Computer and Information Sciences, Temple University, Philadelphia, PA, United States.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary

This study introduces NARS, an adaptive AI system with self-knowledge and self-control mechanisms. Its non-axiomatic logic enables learning from experience, crucial for general-purpose intelligence.

Keywords:
consciousnessgeneral intelligencenon-axiomatic logicself-awarenessself-controlself-organization

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Computer Science

Background:

  • General-purpose intelligent systems require self-related mechanisms for adaptation.
  • Existing systems often lack robust self-knowledge and self-control.
  • The NARS system is designed for adaptability with limited knowledge and resources.

Purpose of the Study:

  • To describe and discuss the self-related mechanisms of the NARS system.
  • To explore how a general-purpose intelligent system can develop a notion of 'self'.
  • To present a preliminary implementation of self-mechanisms in NARS.

Main Methods:

  • Utilizing a central reasoning-learning process based on non-axiomatic logic.
  • Grounding concept meaning in the system's empirical experience.
  • Implementing self-perception and self-control through internal mental operations.

Main Results:

  • NARS exhibits adaptive behavior using a revisable belief system.
  • Self-knowledge in NARS is acquired, constructive, incomplete, and subjective.
  • Self-control is achieved via mental operations that augment automatic inference control.

Conclusions:

  • A 'self' is essential for general-purpose intelligent systems.
  • Self-knowledge and functions develop progressively through experience.
  • NARS demonstrates a foundational implementation of these self-related mechanisms.