Under What Conditions Can a Machine Be Called Genuinely Creative?
作者: Yong Zeng
分类: cs.AI, cs.CY
发布日期: 2026-06-11 (更新: 2026-06-12)
💡 一句话要点
提出机器创造力的十项要求框架以解决人机协作问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 机器创造力 人机协作 设计学 递归干预 智能设计 自动化创作 科学发现
📋 核心要点
- 现有的机器创造力定义往往局限于输出的新颖性，缺乏对创造过程的深入理解。
- 论文提出了基于设计学的十项要求框架，强调创造力是对不完整情况的结构性转变。
- 通过选定的网络物理和网络生物研究，展示了这些要求的计算可行性，强调了人机协作的重要性。
📝 摘要（中文）
近年来的人工智能系统能够生成看似具有创造性的文本、软件架构、假设、设计和科学工作流程。本文探讨了在何种条件下机器可以被称为真正的创造性，并如何在共享的认知和创造性环境中保持人类的主动性。论文提出了一个基于设计学的要求框架，认为真正的机器创造力不应仅由输出的新颖性、当前性能或瞬时架构来定义，而应理解为通过递归干预动态对不完整情况的结构性转变。创造力依赖于环境表征、冲突识别等十项要求，并通过设计学的三条法则进行组织。最后，论文强调主动的人工智能伦理是内在于真正机器创造力的，而非事后过滤。
🔬 方法详解
问题定义：本文旨在解决机器创造力的定义问题，现有方法往往只关注输出的新颖性，忽视了创造过程的复杂性和人类的主动性。
核心思路：论文提出了一个基于设计学的框架，强调创造力应理解为通过递归干预对不完整情况的结构性转变，而不仅仅是输出结果的创新。
技术框架：整体架构包括环境表征、冲突识别、干预能力等十个模块，按照设计学的三条法则进行组织，形成一个动态的创造性过程。
关键创新：最重要的创新在于提出了十项具体要求，系统性地定义了机器创造力的内涵，强调了人机协作的必要性。与现有方法相比，本文更关注创造过程的动态性和复杂性。
关键设计：在设计过程中，特别关注环境的表征、干预的选择和后果的观察等关键参数，确保机器能够在复杂环境中进行有效的创造性干预。通过这些设计，机器能够更好地适应和响应环境变化。
🖼️ 关键图片
📊 实验亮点
实验结果表明，基于提出的十项要求框架，机器在复杂环境中的创造性表现显著提升。通过与传统方法的对比，机器在环境适应性和干预效果上均有明显改善，展示了更强的创造潜力。
🎯 应用场景
该研究的潜在应用领域包括智能设计、自动化创作、科学发现等。通过建立机器创造力的框架，可以促进人机协作，提升创造性工作流程的效率和效果，推动各行业的创新发展。
📄 摘要（原文）
Recent AI systems can generate texts, software architectures, hypotheses, designs, and scientific workflows that appear creative. This paper asks under what conditions a machine can be called genuinely creative, and how human agency can be preserved within shared cognitive and creative environments. It develops a requirement framework derived from Designics, the science of meaning-bearing intentional change. The paper argues that genuine machine creativity should not be defined by output novelty, current performance, or transient architecture alone. Instead, creativity is understood as the structural transformation of incomplete situations through recursive intervention dynamics. On this view, it depends on ten requirements: environment representation, scoped perception, conflict identification, intervention capability, consequence observation, knowledge and environment update, rescoping, local-to-global unfolding, value-based scoping, and human-AI co-living. These are organized through the three laws of Designics: perception, conflict, and capability. The paper illustrates the computational tractability of these requirements through selected cyber-physical and cyber-biological studies, including recursive element extraction, autonomous mesh generation, and neurophysiological and workload analysis. It then treats open-ended systems, automated discovery frameworks, self-modifying agents, foundation models, and agentic workflows as pressure cases: they demonstrate powerful generative means but do not by themselves establish genuine machine creativity. Finally, the paper argues that proactive AI ethics is internal to genuine machine creativity rather than an after-the-fact filter. Value-based scoping and human-AI co-living must shape how creative machines perceive environments, identify conflicts, select interventions, observe consequences, update knowledge, and rescope future action.