PROTECT - A Deployed Game-Theoretic System for Strategic Security Allocation for the United States Coast Guard

被引:24
作者
An, Bo [1 ]
Shieh, Eric
Yang, Rong [1 ]
Tambe, Milind [2 ]
Baldwin, Craig
DiRenzo, Joseph [3 ,4 ]
Maule, Ben [3 ]
Meyer, Garrett [5 ]
机构
[1] Univ So Calif, Dept Comp Sci, Los Angeles, CA 90089 USA
[2] Univ So Calif, TEAMCORE Res Grp, Los Angeles, CA 90089 USA
[3] Coast Guard Atlantic Area, Operat Anal Div, Portsmouth, VA USA
[4] Joint Forces Staff Coll, Norfolk, VA USA
[5] US Coast Guard Sector Boston, Incident Management Div, Boston, MA USA
关键词
25;
D O I
10.1609/aimag.v33i4.2401
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
While three deployed applications of game theory for security have recently been reported, we as a community of agents and AI researchers remain in the early stages of these deployments; there is a continuing need to understand the Core principles for innovative security applications of game theory. Toward that end, this article presents PROTECT a game-theoretic system deployed by the United States Coast Guard (USCG) in the Port of Boston for scheduling its patrols. USCG has termed the deployment of PROTECT in Boston a success; PROTECT is currently being tested in the Port of New York, with the potential for nationwide deployment. PROTECT is premised on an attacker-defender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior - to the best of our knowledge, this is the first real-world deployment of the QR model. Second, to improve PROTECT's efficiency, we generate a compact representation of the defender's strategy space, exploiting equivalence and dominance. Third, we show how to practically model a real maritime patrolling problem as a Stackelberg game. Fourth, our experimental results illustrate that PROTECT's QR model more robustly handles real-world uncertainties than a perfect rationality model. Finally, in evaluating PROTECT, this article for the first time provides real-world data: comparison of human-generated versus PROTECT security schedules, and results from an Adversarial Perspective Team's (human mock attackers) analysis.
引用
收藏
页码:96 / 110
页数:15
相关论文
共 24 条
[1]  
Agmon N, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1811
[2]  
[Anonymous], 1991, Game Theory
[3]  
[Anonymous], 2012, P 11 INT C AUT AG MU
[4]  
Basilico N., 2009, P 8 INT C AUT AG MUL, P500
[5]  
Blair D.B., 2010, ANN THREAT ASSESSMEN
[6]  
Camerer C. F., 2011, Behavioral game theory: Experiments in strategic interaction
[7]  
Conitzer V., 2006, THE EC, P82, DOI DOI 10.1145/1134707.1134717
[8]  
Dozier K., 2011, B LADEN TROVE DOCUME
[9]  
GILPIN A, 2009, THESIS CARNEGIE MELL
[10]   Software Assistants for Randomized Patrol Planning for the LAX Airport Police and the Federal Air Marshal Service [J].
Jain, Manish ;
Tsai, Jason ;
Pita, James ;
Kiekintveld, Christopher ;
Rathi, Shyamsunder ;
Tambe, Milind ;
Ordonez, Fernando .
INTERFACES, 2010, 40 (04) :267-290