Invention Grant
- Patent Title: Adversarial reinforcement learning system for simulating security checkpoint environments
-
Application No.: US16864826Application Date: 2020-05-01
-
Publication No.: US11423157B2Publication Date: 2022-08-23
- Inventor: Brian Jacob Lewis , Jason Adam Deich , Stephen John Melsom , Kara Jean Dodenhoff , William Tyler Niggel
- Applicant: NOBLIS, INC.
- Applicant Address: US VA Reston
- Assignee: NOBLIS, INC.
- Current Assignee: NOBLIS, INC.
- Current Assignee Address: US VA Reston
- Agency: Morrison & Foerster LLP
- Main IPC: G06F21/00
- IPC: G06F21/00 ; G06F21/57 ; G06F21/56

Abstract:
An adversarial reinforcement learning system is used to simulate a security checkpoint. The system includes a simulation engine configured to simulate a security checkpoint and various threat objects and threat-mitigation objects therein. The system further includes an attack model configured to control threat objects in the simulation and a defense model configured to control threat-mitigation objects in the simulation. A first portion of the simulation is executed by the simulation engine in order to generate an outcome of the first portion of the simulation. The defense model then generates a threat-mitigation input to control threat-mitigation objects in a subsequent portion of the simulation, and the attack model then generates a threat input to control threat objects in the subsequent portion of the simulation, wherein the inputs are based in part on the outcome of the first portion of the simulation.
Information query