SPECTRE: A Game Theoretic Framework for Preventing Collusion in Security Games

被引：0

作者：

Gholami, Shahrzad ^{[1
]}

Wilder, Bryan ^{[1
]}

Brown, Matthew ^{[1
]}

Sinha, Arunesh ^{[1
]}

Sintov, Nicole ^{[1
]}

Tambe, Milind ^{[1
]}

机构：

[1] Univ Southern Calif, 941 Bloom Walk, Los Angeles, CA 90007 USA

来源：

AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS | 2016年

关键词：

Game Theory; Stackelberg Security Games; Human Behavior Models; Cooperation Mechanism; Collusion;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Several models have been proposed for Stackelberg security games (SSGs) and protection against perfectly rational and bounded rational adversaries; however, none of these existing models addressed the destructive cooperation mechanism between adversaries. SPECTRE (Strategic Patrol planner to Extinguish Collusive ThREats) takes into account the synergistic destructive collusion among two groups of adversaries in security games. This framework is designed for the purpose of efficient patrol scheduling for security agents in security games in presence of collusion and is mainly build up on game theoretic approaches, optimization techniques, machine learning methods and theories for human decision making under risk. The major advantage of SPECTRE is involving real world data from human subject experiments with participants on Amazon Mechanical Turk (AMT).

引用

页码：1498 / 1500

页数：3