Self in NARS, an AGI System - Wang, Pei; Li, Xiang; Hammer, Patrick (Frontiers in Robotics and AI, 2018)
This article describes and discusses the self-related mechanisms of a general-purpose intelligent system, NARS. This system is designed to be adaptive and to work with insufficient knowledge and resources. The system’s various cognitive functions are uniformly carried out by a central reasoning-learning process following a “non-axiomatic” logic. This logic captures the regularities of human empirical reasoning, where all beliefs are revisable according to evidence, and the meaning of concepts are grounded in the system’s experience. NARS perceives its internal environment basically in the same way as how it perceives its external environment although the sensors involved are completely different. Consequently, its self-knowledge is mostly acquired and constructive, while being incomplete and subjective. Similarly, self-control in NARS is realized using mental operations, which supplement and adjust the automatic inference control routine. It is argued that a general-purpose intelligent system needs the notion of a “self,” and the related knowledge and functions are developed gradually according to the system’s experience. Such a mechanism has been implemented in NARS in a preliminary form.