提出智能场理论框架，用三原则描述目标驱动的动态随机系统。
A Framework for Objective-Driven Dynamical Stochastic Fields
- 基于完备性、局部性和目的性三原则构建理论框架
- 首次形式化定义目标导向的动态随机场机制
- 适合对智能系统建模与AI应用设计感兴趣的读者
场为描述由相互作用且动态变化的组件构成的复杂系统提供了灵活方法。特别地，某些动态随机系统会表现出以达成特定目标为导向的行为，我们称之为‘智能场’。然而，由于其内在复杂性，目前仍难以对这类系统建立正式的理论描述，也难以有效转化为实际应用。本文提出了三个基础性原则，以建立理解智能场的理论框架：完备性、局部性和目的性。同时，从人工智能应用视角探索了此类场的设计方法。本初步研究旨在为未来在理论发展与实际应用方面深入理解并利用目标驱动的动态随机场奠定基础。
原文摘要 · Abstract (English)
Fields offer a versatile approach for describing complex systems composed of interacting and dynamic components. In particular, some of these dynamical and stochastic systems may exhibit goal-directed behaviors aimed at achieving specific objectives, which we refer to as $\textit{intelligent fields}$. However, due to their inherent complexity, it remains challenging to develop a formal theoretical description of such systems and to effectively translate these descriptions into practical applications. In this paper, we propose three fundamental principles to establish a theoretical framework for understanding intelligent fields: complete configuration, locality, and purposefulness. Moreover, we explore methodologies for designing such fields from the perspective of artificial intelligence applications. This initial investigation aims to lay the groundwork for future theoretical developments and practical advances in understanding and harnessing the potential of such objective-driven dynamical stochastic fields.
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