智能系统需在直觉与反思间动态切换以达成目标
Strategic Reflectivism In Intelligent Systems
- 提出战略反思主义，主张智能体应根据目标灵活切换直觉与反思
- 强调直觉与反思的权衡比模型规模更关键，适用于人机协同
- 融合实用主义哲学，为人工智能与人类认知提供统一框架
20世纪后期，理性之争引发了对直觉与反思思维本质与规范的讨论。这些争论源于20世纪中期的有限理性理论，挑战了自19世纪以来的理想化理性观。如今，21世纪的认知科学家正将由此产生的双过程理论应用于人工智能，是时候重新审视这段历史的教训。本文综合旧有思想与近期关于人类和机器的实验成果，提出战略反思主义：智能系统（无论是人类还是人工）的关键在于在直觉与反思推理之间进行务实切换，以最优方式实现相互竞争的目标。该理论植根于美国实用主义，超越模型规模或思维链等表面指标，适用于个体与集体智能系统（包括人机团队），且随着对直觉与反思价值理解的加深，变得日益可操作。
原文摘要 · Abstract (English)
By late 20th century, the rationality wars had launched debates about the nature and norms of intuitive and reflective thinking. Those debates drew from mid-20th century ideas such as bounded rationality, which challenged more idealized notions of rationality observed since the 19th century. Now that 21st century cognitive scientists are applying the resulting dual pro-cess theories to artificial intelligence, it is time to dust off some lessons from this history. So this paper synthesizes old ideas with recent results from experiments on humans and machines. The result is Strategic Reflec-tivism, the position that one key to intelligent systems (human or artificial) is pragmatic switching between intuitive and reflective inference to opti-mally fulfill competing goals. Strategic Reflectivism builds on American Pragmatism, transcends superficial indicators of reflective thinking such as model size or chains of thought, applies to both individual and collective intelligence systems (including human-AI teams), and becomes increasingly actionable as we learn more about the value of intuition and reflection.
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