NARS as an AGI

Objective and Path

1. The objective of A(G)I

Historical background: is "AI" one problem or many problems?

Major differences between AGI and mainstream AI: to build general-purpose and complete models.

Different understandings (working definitions) of intelligence: focus on structure, behavior, capability, function, or principle?

The advantage of seeing "intelligence" as defined by certain principles: simplicity, elegance, consistency, identity

Working definition: Intelligence is the ability of adapting to the environment while working with insufficient knowledge and resources. An intelligent system should rely on finite processing capacity, work in real time, open to unexpected tasks, and learn from experience.

2. Overall strategy

Different forms of "divide-and-conquer": hybrid, integrated, and unified

The selection of strategy depends on the objective of the research.

The unified approach still allows incremental design and development, and takes "intelligence" as a matter of degree.

3. Formal models

An AGI project should be described on (at least) three levels:
  1. as a theory of intelligence, in a natural language,
  2. as a model of the theory, in a symbolic (formal) language,
  3. as an implementation of the model, in a programming language (plus hardware, if necessary).

Three major traditions of formalization: dynamical system, computational system, and reasoning system.

The framework of a reasoning system has the following advantages:

4. Types of reasoning system

According to the assumptions about knowledge and resources, three types of inference systems can be distinguished:

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