能动人工智能：面向复杂系统的以决策为中心的架构
Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems
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中文总结 AI 辅助
本研究提出能动人工智能框架，通过组织世界、站点世界等四个角色构建以决策为中心的架构，拓展AI前沿，为企业与工业复杂系统的可靠AI部署提供新方向。
中文摘要 AI 辅助
随着人工智能（AI）不断发展成熟，近期的AI实践已超越了大语言模型（LLMs）及文本或图像生成任务，正越来越多地整合工具、智能体（agents）与控制系统，以解决真实的商业和工业问题。然而，由于真实商业与工业运营中存在可靠性、可行性、弹性及责任性等要求，AI的能力在这些现实复杂系统中并未得到验证。本研究综合了相关领域的研究成果，提出能动人工智能（Enactive AI）作为面向企业与工业推理、站点级决策支持及执行反馈的概念框架。该框架由四个互补角色构成：组织世界（Organizational World）从战略制度层面定义企业的运营管理逻辑与组织行为世界模型；站点世界（Site World）从运营实现层面定义物理边界内的工业优化与执行世界模型；模式智能（Schema Intelligence）为两个世界模型提供耦合机制，通过这两个模型整合各类AI应用；能动决策周期（Enactive Decision Cycle）触发自演化动态过程，以更新和审计整个框架。通过在复杂系统中突出决策智能，能动人工智能将AI的前沿从模型能力拓展至系统感知的行动，为可扩展、可治理且具有社会价值的AI部署开辟了新可能。能动人工智能指向这样一个未来：AI的进步不仅由模型能生成或自动化的内容衡量，更由智能系统在塑造现代生活的复杂系统中，支持关键行动、负责任治理及持久社会价值的可靠性来衡量，我们认为这将定义企业级与工业复杂系统AI研究的下一个前沿。
英文摘要
As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve real business and industrial problems. However, the power of AI is not verified under these real-world complex systems for various reasons, considering reliability, feasibility, resilience, and responsibility requirements in real commercial and industrial operations. This study synthesizes adjacent research and introduces Enactive AI as a conceptual framework for enterprise and industry reasoning, site-level decision support, and execution feedback. Four complementary roles organize the framework: an Organizational World defines operations management logic and an organizational behavior world model behind an enterprise from a strategic-institutional horizon; a Site World defines a physically bounded industrial optimization and execution world model from an operational-realization horizon; Schema Intelligence provides the coupling mechanism between two world models to weave various AI applications via two models; and Enactive Decision Cycle triggers the self-evolving dynamic process to update and audit the entire framework. By foregrounding decision intelligence in complex systems, Enactive AI expands the frontier of AI from model capability to system-aware action, opening new possibilities for scalable, governable, and socially valuable AI deployment. Enactive AI points toward a future in which AI progress is measured not only by what models can generate or automate, but by how reliably intelligent systems can support consequential action, responsible governance, and durable social value in the complex systems that shape modern life, which we believe will define the next frontier of AI research for enterprise-level and industrial complex systems.
