@conference {1502902,
	title = {Robust protection of fisheries with COmPASS },
	booktitle = {Innovative applications of Artificial Intelligence (IAAI)},
	year = {2014},
	abstract = {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.},
	author = {William B. Haskell and Kar, Debarun and Fang, Fei and Tambe, Milind and Cheung, Sam and Denicola, Elizabeth}
}
