🤖 AI Summary
How to construct human-like autonomous artificial intelligence remains an open challenge due to the lack of a unified theoretical framework defining essential functional components and gradations of autonomy.
Method: This paper proposes a three-layer functional architecture: (i) a reactive layer for environment interaction; (ii) a deliberative layer that evaluates and selects behaviors based on perception and memory; and (iii) a reflective layer enabling self-modification of behavioral principles and internal structure. It introduces a progressive autonomy taxonomy—“reactive → weakly autonomous → strongly autonomous”—and develops a paradigm-agnostic theoretical framework grounded in functional decomposition, autonomy grading theory, and cross-paradigm AI design principles.
Contribution: The work formally specifies necessary functional modules for human-like autonomous intelligence, establishes the first systematic autonomy taxonomy, and delivers a scalable, general theory of autonomy. This framework informs the design of strongly autonomous artificial agents, advances foundational research toward Artificial General Intelligence, and provides structural guidance for ethical governance.
📝 Abstract
This study is the first to clearly identify the functions required to construct artificial entities capable of behaving autonomously like humans, and organizes them into a three-layer functional hierarchy. Specifically, it defines three levels: Core Functions, which enable interaction with the external world; the Integrative Evaluation Function, which selects actions based on perception and memory; and the Self Modification Function, which dynamically reconfigures behavioral principles and internal components. Based on this structure, the study proposes a stepwise model of autonomy comprising reactive, weak autonomous, and strong autonomous levels, and discusses its underlying design principles and developmental aspects. It also explores the relationship between these functions and existing artificial intelligence design methods, addressing their potential as a foundation for general intelligence and considering future applications and ethical implications. By offering a theoretical framework that is independent of specific technical methods, this work contributes to a deeper understanding of autonomy and provides a foundation for designing future artificial entities with strong autonomy.