Scaling-up stackelberg security games applications using approximations

• Main Authors: SINHA, Arunesh, SCHLENKER, Aaron, DMELLO, Donnabell, TAMBE, Milind
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4794; https://ink.library.smu.edu.sg/context/sis_research/article/5797/viewcontent/Scaling_up_Stackelberg_Security_Games_Applications_using_Approximations_1_.pdf
• Institution: Singapore Management University

Stackelberg Security Games (SSGs) have been adopted widely for modeling adversarial interactions, wherein scalability of equilibrium computation is an important research problem. While prior research has made progress with regards to scalability, many real world problems cannot be solved satisfactorily yet as per current requirements; these include the deployed federal air marshals (FAMS) application and the threat screening (TSG) problem at airports. We initiate a principled study of approximations in zero-sum SSGs. Our contribution includes the following: (1) a unified model of SSGs called adversarial randomized allocation (ARA) games, (2) hardness of approximation for zero-sum ARA, as well as for the FAMS and TSG sub-problems, (3) an approximation framework for zero-sum ARA with instantiations for FAMS and TSG using intelligent heuristics, and (4) experiments demonstrating the significant 1000x improvement in runtime with an acceptable loss.