A Framework for Objective-Driven Dynamical Stochastic Fields
This work provides a foundational framework for understanding and applying intelligent fields in AI, though it is incremental as it lays groundwork for future developments.
The paper tackles the challenge of developing a formal theoretical description for intelligent fields, which are dynamical stochastic systems with goal-directed behaviors, by proposing three fundamental principles: complete configuration, locality, and purposefulness, and explores methodologies for designing such fields from an AI perspective.
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.