用逻辑推理追踪关掉AIS的暗船，精准定位其隐藏轨迹。
Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories
- 基于反事实推理和规则学习构建海事行为模型
- 实现接近100%暗船召回率，搜索范围更小
- 适合海上安全监控与反走私分析人员
航运业中的不法行为者在关闭船舶自动识别系统（AIS）后从事非法活动，使分析师难以发现这些船只。现有机器学习方法仅能预测短期内的暗船位置。本文借鉴对抗性代理的反事实推理思想，结合归因推理、逻辑编程与规则学习，提出一种高效方法，在显著缩小搜索区域的同时，逼近对暗船的完全召回。研究提出了基于逻辑的船舶行为推理范式、反事实查询机制、自动化规则提取方法，并进行了全面实验验证。
原文摘要 · Abstract (English)
Bad actors in the maritime industry engage in illegal behaviors after disabling their vessel's automatic identification system (AIS) - which makes finding such vessels difficult for analysts. Machine learning approaches only succeed in identifying the locations of these ``dark vessels'' in the immediate future. This work leverages ideas from the literature on abductive inference applied to locating adversarial agents to solve the problem. Specifically, we combine concepts from abduction, logic programming, and rule learning to create an efficient method that approaches full recall of dark vessels while requiring less search area than machine learning methods. We provide a logic-based paradigm for reasoning about maritime vessels, an abductive inference query method, an automatically extracted rule-based behavior model methodology, and a thorough suite of experiments.
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