English(EN) Directing Open-Ended Evolution in Artificial Life via Multi-Scale Path Divergence

新指标以受物理学启发的途径指导人工智能生命体的进化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-12 22:24

研究人员开发了一种名为多尺度路径发散（MSPD）的新指标，用于指导人工智能生命体系统的开放式进化。与以往的黑盒复杂度指标不同，MSPD是一个受重整化群理论启发的显式公式，用于量化局部转移定律中异质性的时间多尺度组织。该指标既可作为无梯度适应度函数，也可作为分析工具，在Flow-Lenia和细胞自动机等各种基底上，通过经验性成功证明其产生的复杂度得分高于随机参数。 AI

排序理由该集群包含一篇详细介绍人工智能生命体研究新指标的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Andrey Ustyuzhanin · 2026-06-12 22:24

通过多尺度路径分歧指导开放式人工生命进化

Open-ended evolution (OEE) in artificial life is typically driven by uninterpretable, black-box neural-network complexity metrics, leaving life-like systems disconnected from physical theories of complexity. We introduce MSPD (Multi-Scale Path Divergence, denoted DP ), a renormal…

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通过多尺度路径分歧指导开放式人工生命进化

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