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When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing.
Fei
Fang, Peter Stone, and Milind
Tambe.
In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), July 2015.
Winner of Computational Sustainability Track Outstanding Paper Award at IJCAI 2015
[PDF]456.7kB [postscript]983.9kB [slides.pptx]6.2MB
Building on the successful applications of Stackelberg Security Games (SSGs) to protect infrastructure, researchers have begun focusing on applying game theory to green security domains such as protection of endangered animals and fish stocks. Previous efforts in these domains optimize defender strategies based on the standard Stackelberg assumption that the adversaries become fully aware of the defender's strategy before taking action. Unfortunately, this assumption is inappropriate since adversaries in green security domains often lack the resources to fully track the defender strategy. This paper (i) introduces Green Security Games (GSGs), a novel game model for green security domains with a generalized Stackelberg assumption; (ii) provides algorithms to plan effective sequential defender strategies --- such planning was absent in previous work; (iii) proposes a novel approach to learn adversary models that further improves defender performance; and (iv) provides detailed experimental analysis of proposed approaches.
@InProceedings{IJCAI15-fei,
title={When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing},
author={Fei Fang and Peter Stone and Milind Tambe},
booktitle={Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)},
year={2015},
month={July},
abstract={
Building on the successful applications of Stackelberg
Security Games (SSGs) to protect infrastructure,
researchers have begun focusing on applying game theory to
green security domains such as protection of endangered
animals and fish stocks. Previous efforts in these domains
optimize defender strategies based on the standard
Stackelberg assumption that the adversaries become fully
aware of the defender's strategy before taking
action. Unfortunately, this assumption is inappropriate
since adversaries in green security domains often lack the
resources to fully track the defender strategy. This paper
(i) introduces Green Security Games (GSGs), a novel game
model for green security domains with a generalized
Stackelberg assumption; (ii) provides algorithms to plan
effective sequential defender strategies --- such planning
was absent in previous work; (iii) proposes a novel
approach to learn adversary models that further improves
defender performance; and (iv) provides detailed
experimental analysis of proposed approaches.
},
wwwnote={Winner of <b>Computational Sustainability Track Outstanding Paper Award</b> at <a href="http://ijcai-15.org/">IJCAI 2015</a>},
}
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