ARMOR: a cool acronym at a cool airport

Like others, I’ll be using this week’s blog post to give some background on my upcoming presentation. My paper describes an application of operations research to airport security.

The Los Angeles International Airport (known as “LAX” in some niche social circles) is the second busiest airport in the United States in terms of passenger boarding. Boasting nearly 40 million departures in 2015, LAX is believed to be an attractive target for terrorists. Its role in domestic and international transportation cannot be understated. As police and security resources are limited, completely securing the airport is impossible. Even if full security coverage were possible, it would be a logistical nightmare that would seriously delay passengers.

The authors of the paper developed ARMOR (Assistant for Randomized Monitoring over Routes) as an application to help security agencies determine where to allocate their limited resources. It is tailored to meet two key challenges in airport security. First, it provides randomization in its resource allocation. When adversaries are able to recognize patterns in security, they are able to exploit them. Randomization adds a much-desired layer of uncertainty to the adversary’s planning. Second, ARMOR addresses security forces’ uncertainty in information about the adversary. In the context if airport security, threats can manifest in numerous ways (bombs, hijackings, etc.) which require different resources for detection. ARMOR works to mitigate this by considering multiple threats simultaneously and formulating the best overall security plan.

ARMOR is based on the theory of Stackelberg games. In a Stackelberg game, a leader first makes decisions, to which a follower reacts in an attempt to optimize his or her reward. Consider the payoff table below for a Stackelberg game. Let L_1 and L_2 denote possible initial actions for the leader. Similarly, let F_1 and F_2 denote possible recourse actions for the follower.


In a simultaneous game, there exists a Nash equilibrium where the leader chooses action L_1 and the follower chooses action F_1. In this situation, neither player can benefit unilaterally by changing his or her decision. However, we are not considering Nash equilibria in this situation; rather, we allow the leader to choose an action first, and the follower to selfishly react thereto. With this paradigm, the leader would select L_2, because it is known that the follower will subsequently select F_2 to obtain the maximum payoff. This strategy gives the leader a payoff of 3. If we allow the leader to select each action with a fixed probability, selecting L_1 and L_2 each with probability 0.5 would result in a reward of 3.5. The follower, capable of choosing only one action, would select F_2.

In a Bayesian Stackelberg game, we allow a set of leaders and a set of followers to play against one another. ARMOR incorporates a Bayesian Stackelberg game with one leader (airport security) and multiple followers (the variety of security threats). Next week, we’ll discuss how to model this Bayesian Stackelberg game first as a mixed-integer quadratic program, then as a mixed-integer linear program. We’ll discuss some of the challenges of creating the ARMOR software and also how it performed “in the field.”

Pita, James, et al. “Deployed ARMOR protection: the application of a game theoretic model for security at the Los Angeles International Airport.”Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track. International Foundation for Autonomous Agents and Multiagent Systems, 2008.


2 thoughts on “ARMOR: a cool acronym at a cool airport

  1. This is really interesting. I like the idea of using optimization techniques to thwart terrorists. I’m curious, though, how implementable their suggested methodology is. I’m sure you’ll go into a lot more detail in your presentation today, but it seems next-to-impossible to come up with a good airport security plan, particularly in a place as big as LAX. I’m guessing the authors are suggesting using ARMOR in tandem with other security measures? In an ideal scenario, how would ARMOR work? Has anything like this ever been tested (even on a small-scale model), and has it been shown to be effective? I’m looking forward to hearing more about the methodology in your presentation!


    1. ARMOR was actually created as an application for the LAWA police to use! In the presentation, we’ll discuss the previous method used to allocate security resources at LAX (hint: it was awful). The authors performed an analysis to compare their solving technique to other techniques, and they showed it was preferable by multiple metrics.

      Since its implementation at LAX, ARMOR has been successful in helping airport authorities arrest numerous individuals. That certainly doesn’t prove ARMOR is superior to other security allocation methods in every way, but it definitely is interesting.


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