When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Computational Sustainability. Pages 2589-2594.
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.
@inproceedings{ijcai2015p367,
title = {When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing},
author = {Fang, Fei and Stone, Peter and Tambe, Milind},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {2589--2594},
year = {2015},
month = {7},
note = {Special Track on Computational Sustainability},
url = {https://ijcai.org/proceedings/2015/367},
}
TY - CPAPER TI - When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing AU - Fang, Fei AU - Stone, Peter AU - Tambe, Milind T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 2589 EP - 2594 UR - https://ijcai.org/proceedings/2015/367 ER -Copied
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