Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories
This addresses illegal maritime activities by providing analysts with a more efficient tool for tracking dark vessels, though it appears incremental as it builds on existing abductive inference and logic programming concepts.
The paper tackles the problem of locating maritime vessels that disable their automatic identification system (AIS) to evade detection, developing a method that achieves near-full recall of these 'dark vessels' while requiring less search area than existing machine learning approaches.
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.