Security Games in the Field: Deployments on a Transit System


F. M. Delle Fave, M. Brown, C. Zhang, E. Shieh, A.X. Jiang, H. Rosoff, M. Tambe, and J.P. Sullivan. 2014. “Security Games in the Field: Deployments on a Transit System .” In: Lecture Notes in Artificial Intelligence. Springer. In Press.


This paper proposes the Multi-Operation Patrol Scheduling System (MOPSS), a new system to generate patrols for transit system. MOPSS is based on five contributions. First, MOPSS is the first system to use three fundamentally different adversary models for the threats of fare evasion, terrorism and crime, generating three significantly different types of patrol schedule. Second, to handle uncertain interruptions in the execution of patrol schedules, MOPSS uses Markov decision processes (MDPs) in its scheduling. Third, MOPSS is the first system to account for joint activities between multiple resources, by employing the well known SMART security game model that tackles coordination between defender’s resources. Fourth, we are also the first to deploy a new Opportunistic Security Game model, where the adversary, a criminal, makes opportunistic decisions on when and where to commit crimes. Our fifth, and most important, contribution is the evaluation of MOPSS via real-world deployments, providing data from security games in the field.
See also: 2014