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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
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.
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.
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