用游戏化方法实时识别最优边界，仅需公开数据
A Sequential Decision-Making Model for Perimeter Identification
- 将边界搜索建模为智能体与环境的序贯博弈
- 仅依赖公开信息实现实时边界优化
- 适合城市规划、交通管理等实时决策场景
边界识别涉及确定指定区域或地带的边界，需要交通流监测、控制或优化。现有多种方法和技术可用于准确界定这些边界，但通常需要专用设备、精确地图或全面数据才能有效界定问题范围。本研究提出一种用于边界搜索的序贯决策框架，能够在实时环境下高效运行，且仅需公开可获取的信息。我们将边界搜索视为智能体与人工环境之间的博弈，智能体的目标是通过逐步改进当前边界来识别最优边界。我们详细描述了该博弈模型，并讨论其在确定最优边界定义方面的适应性。最终，通过一个真实场景展示了该模型的有效性，突出显示了相应最优边界的识别。
原文摘要 · Abstract (English)
Perimeter identification involves ascertaining the boundaries of a designated area or zone, requiring traffic flow monitoring, control, or optimization. Various methodologies and technologies exist for accurately defining these perimeters; however, they often necessitate specialized equipment, precise mapping, or comprehensive data for effective problem delineation. In this study, we propose a sequential decision-making framework for perimeter search, designed to operate efficiently in real-time and require only publicly accessible information. We conceptualize the perimeter search as a game between a playing agent and an artificial environment, where the agent's objective is to identify the optimal perimeter by sequentially improving the current perimeter. We detail the model for the game and discuss its adaptability in determining the definition of an optimal perimeter. Ultimately, we showcase the model's efficacy through a real-world scenario, highlighting the identification of corresponding optimal perimeters.
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