arXiv cs.AI· Adamo Cerioli, Vito D. P. Servedio, Gabriele Cerioli, Antonio Villani, Carlo Metta·· 2 小时前AI 评分46
AI 教人类应对围棋中的复杂性
AI Teaches Humans to Handle Complexity in the Game of Go
AI 导读
一项 arXiv 研究用围棋作为实验平台，通过空间依赖矩阵谱半径定义“战略张力”指标，分析人类与 AI 的决策权衡。研究发现张力从新手到高手递减，但 AlphaGo 2016 年突破后，1980—2026 年职业对局的张力显著上升，现代职业棋手在二维投影中接近神经网络引擎。作者认为 AI 可作为认知脚手架，提升人类应对高阶复杂性的能力。
正文 · 原文
Abstract:Strategic decision-making in complex environments requires balancing local stability against global coordination. Using the game of Go as a bounded empirical testbed, we investigate how biological and artificial agents navigate this trade-off by formalizing a network-derived metric of strategic tension defined via the spectral radius of spatial dependency matrices. Across human skill progression, tension systematically decays from novices to masters as uncoordinated tactical skirmishes give way to disciplined territorial stabilization. However, a longitudinal analysis of professional play spanning 1980--2026 reveals a striking historical paradox: following the superhuman breakthrough of AlphaGo in 2016, elite human matches display a marked surge in sustained tension. A two-dimensional projection based on move distance and captures places modern professionals close to neural engines, separating their tension from the local contact fighting of novices and from random noise. Consequently, strategic tension acts as a macroscopic order parameter for decision paradigms rather than raw computational strength. Beyond the game of Go, these findings establish a constructive paradigm for human-AI interaction, indicating how artificial systems can act as cognitive scaffolding that expands biological heuristics and elevates human capacity to navigate higher-order complexity across professional training and decision-making domains.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33323 [cs.AI] |
| (or arXiv:2610.33323v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33323 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Adamo Cerioli [view email]
[v1]
Fri, 23 Oct 2026 21:52:10 UTC (1,287 KB)
来源：arXiv cs.AI · arxiv.org