Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2020
Abstract
Large-scale screening for potential threats with limited resources and capacity for screening is a problem of interest at airports, seaports, and other ports of entry. Adversaries can observe screening procedures and arrive at a time when there will be gaps in screening due to limited resource capacities. To capture this game between ports and adversaries, this problem has been previously represented as a Stackelberg game, referred to as a Threat Screening Game (TSG). Given the significant complexity associated with solving TSGs and uncertainty in arrivals of customers, existing work has assumed that screenees arrive and are allocated security resources at the beginning of the time-window. In practice, screenees such as airport passengers arrive in bursts correlated with flight time and are not bound by fixed timewindows. To address this, we propose an online threat screening model in which the screening strategy is determined adaptively as a passenger arrives while satisfying a hard bound on acceptable risk of not screening a threat. To solve the online problem, we first reformulate it as a Markov Decision Process (MDP) in which the hard bound on risk translates to a constraint on the action space and then solve the resultant MDP using Deep Reinforcement Learning (DRL). To this end, we provide a novel way to efficiently enforce linear inequality constraints on the action output in DRL. We show that our solution allows us to significantly reduce screenee wait time without compromising on the risk.
Keywords
Airport passenger, Linear inequality constraints, Markov Decision Processes, Potential threats, Resource capacity, Screening procedures, Screening strategy, Stackelberg Games
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of 34rd AAAI Conference on Artificial Intelligence (AAAI), New York, 2020 February 7-12
First Page
1
Last Page
10
ISBN
9781577358350
Identifier
10.1609/aaai.v34i02.5599
Publisher
AAAI Press
City or Country
Palo Alto, CA
Citation
SHAH, Sanket; SINHA, Arunesh; VARAKANTHAM, Pradeep; PERRAULT, Andrew; and TAMBE, Millind.
Solving online threat screening games using constrained action space reinforcement learning. (2020). Proceedings of 34rd AAAI Conference on Artificial Intelligence (AAAI), New York, 2020 February 7-12. 1-10.
Available at: https://ink.library.smu.edu.sg/sis_research/5077
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1609/aaai.v34i02.5599