The article analyses foundational principles relevant to the creation of artificial general intelligence (AGI). Intelligence is understood as the ability to create novel skills that allow to achieve goals under previously unknown conditions. To this end, intelligence utilises reasoning methods such as deduction, induction and abduction as well as other methods such as abstraction and classification to develop a world model. The methods are applied to indirect and incomplete representations of the world, which are obtained through perception, for example, and which do not depict the world but only correspond to it. Due to these limitations and the uncertain and contingent nature of reasoning, the world model is constructivist. Its value is functionally determined by its viability, i.e., its potential to achieve the desired goals. In consequence, meaning is assigned to representations by attributing them a function that makes it possible to achieve a goal. This representational and functional conception of intelligence enables a naturalistic interpretation that does not presuppose mental features, such as intentionality and consciousness, which are regarded as independent of intelligence. Based on a phenomenological analysis, it is shown that AGI can gain a more fundamental access to the world than humans, although it is limited by the No Free Lunch theorems, which require assumptions to be made.

本研究针对人工通用智能（AGI）的创建探讨了基础原则，重点分析智能作为在未知条件下实现目标的能力。文章提出了一种新的智能观念，强调通过推理方法与世界模型的构建来实现目标，认为智能的摘要和分类是基于构建主义的，并赋予了不同表征以功能性的意义。这一观点可能推动AGI的实现，提供比人类更基本的世界理解方式。

智能的表征主义、功能主义与自然主义观念作为通用人工智能的基础