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.

翻译：摘要：认知的成熟——从内省到理解他人——长期以来一直是人类发展的标志。本文立场文件认为，若人工智能系统要真正模拟或接近类人交互，尤其是在充满多样智能体的复杂环境中，它们必须首先对自身达成深入细致的理解。通过与人类从自我意识到心智化（亦称心理理论）的发展轨迹进行类比，本文主张自主智能体的内省能力质量对镜像高质量类人理解其他智能体至关重要。尽管反对意见强调实用性、计算效率及伦理关切，本文提出一种融合自指处理算法考虑的开发方法。最终，所勾勒的愿景并非仅关乎会计算的机器，而是能内省、共情与理解的实体，与人类认知的复杂构成和谐统一。