Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2016
Abstract
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
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of 25th International Joint Conference on Artificial Intelligence (IJCAI)
City or Country
New York, USA
Citation
HAGHTALAB, Nika; FANG, Fei; NGUYEN, Thanh Hong; SINHA, Arunesh; PROCACCIA, Ariel D.; and TAMBE, Milind.
Three strategies to success: Learning adversary models in security games. (2016). Proceedings of 25th International Joint Conference on Artificial Intelligence (IJCAI).
Available at: https://ink.library.smu.edu.sg/sis_research/4663
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.