Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories

2025-02-03

AI Summary

Automatic Identification System (AIS) shutdowns create “dark ships,” posing significant challenges for maritime surveillance and law enforcement due to difficulties in localizing such vessels and delayed response times. Method: This paper proposes an abductive reasoning–based maritime region generation method that integrates logic programming (Prolog/Datalog) with rule learning to construct an interpretable framework for modeling anomalous vessel behavior and solving spatial constraint satisfaction problems—automatically inferring the most probable activity regions of dark ships. Contribution/Results: To our knowledge, this is the first application of abductive reasoning to maritime security analytics. The approach achieves near-perfect (≈100%) recall in dark ship detection while reducing the candidate search area by 62% compared to state-of-the-art machine learning methods. It thus significantly enhances both the precision and timeliness of maritime domain awareness and enforcement operations.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Applications

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

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.

Problem

• Ship Tracking
• AIS Off
• Enforcement Challenges

Innovation

• Detective Logic
• Rule Learning
• Ship Tracking