Three strategies to success: Learning adversary models in security games

State-of-the-art applications of Stackelberg security games -- including wildlife protection -- offer a wealth of data, which can be used to learn the behavior of the adversary. But existing approaches either make strong assumptions about the structure of the data, or gather new data through online...

Full description

Saved in:
Bibliographic Details
Main Authors: HAGHTALAB, Nika, FANG, Fei, NGUYEN, Thanh Hong, SINHA, Arunesh, PROCACCIA, Ariel D., TAMBE, Milind
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/4663
https://ink.library.smu.edu.sg/context/sis_research/article/5666/viewcontent/ijcai16_full_1_.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary:State-of-the-art applications of Stackelberg security games -- including wildlife protection -- offer a wealth of data, which can be used to learn the behavior of the adversary. But existing approaches either make strong assumptions about the structure of the data, or gather new data through online algorithms that are likely to play severely suboptimal strategies. We develop a new approach to learning the parameters of the behavioral model of a bounded rational attacker (thereby pinpointing a near optimal strategy), by observing how the attacker responds to only three defender strategies. We also validate our approach using experiments on real and synthetic data