The maturation of cognition, from introspection to understanding others, has
long been a hallmark of human development. This position paper posits that for
AI systems to truly emulate or approach human-like interactions, especially
within multifaceted environments populated with diverse agents, they must first
achieve an in-depth and nuanced understanding of self. Drawing parallels with
the human developmental trajectory from self-awareness to mentalizing (also
called theory of mind), the paper argues that the quality of an autonomous
agent's introspective capabilities of self are crucial in mirroring quality
human-like understandings of other agents. While counterarguments emphasize
practicality, computational efficiency, and ethical concerns, this position
proposes a development approach, blending algorithmic considerations of
self-referential processing. Ultimately, the vision set forth is not merely of
machines that compute but of entities that introspect, empathize, and
understand, harmonizing with the complex compositions of human cognition.

人工智能系统在模拟或接近人类间的相互作用中，特别是在多层次环境中与不同实体进行的情况下，必须首先深入而细致地理解自己，以实现对人类认知的高质量理解。