F Ordonez, M Tambe, J Jara, M Jain, C Kiekintveld, and J Tsai
In Handbook on Operations Research for Homeland Security (Book chapter).
This is the author's version of the work.
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Abstract
Nations and organizations need to secure locations of economic, military,
or political importance from groups or individuals that can cause harm. The fact
that there are limited security resources prevents complete security coverage, which
allows adversaries to observe and exploit patterns in patrolling or monitoring, and
enables them to plan attacks that avoid existing patrols. The use of randomized security
policies that are more difficult for adversaries to predict and exploit can counter
their surveillance capabilities and improve security. In this chapter we describe the
recent development of models to assist security forces in randomizing their patrols
and their deployment in real applications.
The systems deployed are based on fast algorithms for solving large instances of
Bayesian Stackelberg games that capture the interaction between security forces and
adversaries. Here we describe a generic mathematical formulation of these models,
present some of the results that have allowed these systems to be deployed in practice,
and outline remaining future challenges. We discuss the deployment of these
systems in two real-world security applications: 1) The police at the Los Angeles
International Airport uses these models to randomize the placement of checkpoints
on roads entering the airport and the routes of canine unit patrols within the airport
terminals. 2) The Federal Air Marshal Service uses these models to randomize the
schedules of air marshals on international flights.
