Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916602523418624 |
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| author | Bavikadi, Divyagna Lee, Nathaniel Shakarian, Paulo Parvis, Chad |
| author_facet | Bavikadi, Divyagna Lee, Nathaniel Shakarian, Paulo Parvis, Chad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01503 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories Bavikadi, Divyagna Lee, Nathaniel Shakarian, Paulo Parvis, Chad Artificial Intelligence Machine Learning Logic in Computer Science Symbolic Computation 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. |
| title | Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories |
| topic | Artificial Intelligence Machine Learning Logic in Computer Science Symbolic Computation |
| url | https://arxiv.org/abs/2502.01503 |