Robust Protection of Fisheries with COmPASS
Fish stocks around the world are in danger from illegal fishing. In collaboration with the U.S. Coast Guard (USCG), we work to defend fisheries from illegal fisherman (henceforth called Lanchas) in the U.S. Gulf of Mexico. We have developed the COmPASS (Conservative Online Patrol ASSistant) system to design USCG patrols against the Lanchas. In this application, we face a population of Lanchas with heterogeneous behavior who fish frequently. We have some data about these Lanchas, but not enough to fit a statistical model. Previous security patrol assistants have focused on counterterrorism in one-shot games where adversaries are assumed to be perfectly rational, and much less data about their behavior is available. COmPASS is novel because: (i) it emphasizes environmental crime; (ii) it is based on a repeated Stackelberg game; (iii) it allows for bounded rationality of the Lanchas and it offers a robust approach against the heterogeneity of the Lancha population; and (iv) it can learn from sparse Lancha data. We report the effectiveness of COmPASS in the Gulf in our numerical experiments based on real fish data. The COmPASS system is to be tested by USCG.
Top-30
Journals
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Lecture Notes in Computer Science
7 publications, 33.33%
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ACM SIGecom Exchanges
1 publication, 4.76%
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Journal of Ambient Intelligence and Humanized Computing
1 publication, 4.76%
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Annals of Operations Research
1 publication, 4.76%
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Applied Intelligence
1 publication, 4.76%
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Conservation Letters
1 publication, 4.76%
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Springer Proceedings in Mathematics and Statistics
1 publication, 4.76%
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Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
1 publication, 4.76%
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Advanced Theory and Simulations
1 publication, 4.76%
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IEEE Transactions on Cognitive and Developmental Systems
1 publication, 4.76%
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Journal of Cybersecurity
1 publication, 4.76%
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Springer Nature
12 publications, 57.14%
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Wiley
3 publications, 14.29%
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Association for Computing Machinery (ACM)
1 publication, 4.76%
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Institute of Electrical and Electronics Engineers (IEEE)
1 publication, 4.76%
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Oxford University Press
1 publication, 4.76%
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- We do not take into account publications without a DOI.
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