Modeling human adversary decision making in security games: An initial report

Thanh H. Nguyen, James Pita, Rajiv Maheswaran, Milind Tambe, Amos Azaria, Sarit Kraus

Research output: Contribution to conferencePaperpeer-review

Abstract

Motivated by recent deployments of Stackelberg security games (SSGs), two competing approaches have emerged which either integrate models of human decision making into game-theoretic algorithms or apply robust optimization techniques that avoid adversary modeling. Recently, a robust technique (MATCH) has been shown to significantly outperform the leading modeling-based algorithms (e.g., Quantal Response (QR)) even in the presence of significant amounts of subject data. As a result, the effectiveness of using human behaviors in solving SSGs remains in question. We study this question in this paper.

Original languageEnglish
Pages1297-1298
Number of pages2
StatePublished - 2013
Event12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 - Saint Paul, MN, United States
Duration: 6 May 201310 May 2013

Conference

Conference12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013
Country/TerritoryUnited States
CitySaint Paul, MN
Period6/05/1310/05/13

Funding

FundersFunder number
Army Research Office# W911NF-10-1-0185, # W911NF0910206, # W91 INFI 110344

    Keywords

    • Bounded Rationality
    • Game Theory
    • Human Behavior
    • Quantal Response
    • Robust Optimization

    Fingerprint

    Dive into the research topics of 'Modeling human adversary decision making in security games: An initial report'. Together they form a unique fingerprint.

    Cite this