Using Game Theory for Los Angeles Airport Security
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Articles Using Game Theory for Los Angeles Airport Security James Pita, Manish Jain, Fernando Ordóñez, Christopher Portway, Milind Tambe, Craig Western, Praveen Paruchuri, and Sarit Kraus n Security at major locations of economic or political impor tance is a key concern around P the world, particularly given the threat of ter- rotecting national infrastructure such as airports, historical rorism. Limited security resources prevent full landmarks, or a location of political or economic importance is security coverage at all times, which allows a challenging task for police and security agencies around the adversaries to observe and exploit patterns in world, a challenge that is exacerbated by the threat of terrorism. selective patrolling or mon itoring; for exam- Such protection of important locations includes tasks such as ple, they can plan an attack avoiding existing monitoring all entrances or inbound roads and checking pa trols. Hence, randomized patrolling or monitoring is impor tant, but randomization inbound traffic. However, limited resources imply that it is typ- must provide distinct weights to dif ferent ically impossible to provide full security cover age at all times. actions based on their complex costs and Furthermore, adversaries can observe se curity arrangements benefits. To this end, this article describes a over time and exploit any predictable patterns to their advan- promising transition of the lat est in multia- tage. Randomizing schedules for pa trolling, checking, or moni- gent algorithms into a deployed application. toring is thus an important tool in the police arsenal to avoid In particular, it describes a software assistant the vulnerability that comes with predictability. Even beyond agent called AR MOR (assistant for random- protecting infrastructure, ran domized patrolling is important in ized monitoring over routes) that casts this tasks ranging from secu rity on university campuses to normal patrolling and monitoring problem as a Bayesian Stackelberg game, allowing the police beats to border or maritime security (Billante 2003, agent to appropriately weigh the different Paruchuri et al. 2007, Ruan et al. 2005). actions in randomization, as well as uncer- This article focuses on a deployed software assistant agent tainty over adversary types. ARMOR com- that can aid police or other security agencies in randomizing bines two key features. It uses the fastest their security schedules. We face at least three key chal lenges in known solver for Bayesian Stackelberg games building such a software assistant. First, the as sistant must pro- called DOBSS, where the dominant mixed vide quality guarantees in randomization by appropriately strategies enable randomization; and its weighing the costs and benefits of the different options avail- mixed-initiative-based interface allows users able. For example, if an attack on one part of an infrastructure occasionally to adjust or override the auto- will cause economic damage while an attack on another could mated schedule based on their local con- straints. ARMOR has been successfully potentially cost human lives, we must weigh the two options deployed since August 2007 at the Los Ange- differently—giving higher weight (probability) to guarding the les International Airport (LAX) to randomize latter. Second, the assistant must address the uncertainty in checkpoints on the roadways entering the air- information that security forces have about the adversary. port and canine patrol routes within the air- Third, the assistant must en able a mixed-initiative interaction port terminals. This article examines the with potential users rather than dictate a schedule; the assistant information, design choices, challenges, and may be unaware of users’ real-world constraints, and hence evaluation that went into designing ARMOR. users must be able to shape the schedule development. We have addressed these challenges in a software assis tant Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602 SPRING 2009 43 Articles agent called ARMOR (assistant for randomized six-month trial period of ARMOR deployment at mon itoring over routes). Based on game-theoretic LAX. The feedback from police at the end of this principles, ARMOR combines three key features to six-month pe riod was extremely positive; ARMOR address each of the challenges outlined above. will continue to be deployed at LAX and expand to Game theory is a well-established foundational other police activities at LAX. principle within multiagent sys tems to reason about multiple agents, each pursuing its own inter- ests (Fudenberg and Tirole 1991). We build on Security Domain Description these game-theoretic foundations to reason about We will now describe the specific challenges in the two agents—the police force and its adversary—in security problems faced by the LAWA police. LAX1 providing a method of randomization. In particu- is the fifth bus iest airport in the United States and lar, the main contribution of our article is mapping the largest destination airport in the United States, the problem of security scheduling as a Bayesian serving 60–70 million passen gers per year (Stevens Stackelberg game (Conitzer and Sandholm 2006) et al. 2006). LAX is unfortunately also suspected to and solving it through the fastest optimal al go- be a prime terrorist target on the West Coast of the rithm for such games (Paruchuri et al. 2008), United States, with multiple arrests of plotters address ing the first two challenges. The algorithm attempting to attack LAX (Stevens et al. 2006). To used builds on several years of research regarding protect LAX, LAWA police have designed a securi- multiagent systems and security (Paruchuri et al. ty sys tem that utilizes multiple rings of protection. 2005, 2006, 2007). In particular, ARMOR relies on As is evident to anyone traveling through an air- an optimal algorithm called DOBSS (de composed port, these rings include such things as vehicular optimal Bayesian Stackelberg solver) (Paruchuri et checkpoints, police units patrolling the roads to al. 2008). the terminals and inside the terminals (with dogs), While a Bayesian game allows us to address and security screening and bag checks for passen- uncertainty over adversary types, by optimally gers. There are unfortunately not enough resources solving such Bayesian Stackelberg games (which (police offi cers) to monitor every single event at yield optimal randomized strate gies as solutions), the airport; given its size and the number of pas- ARMOR provides quality guarantees on the sched- sengers served, such a level of screen ing would ules generated. These quality guarantees obviously require considerably more personnel and cause do not imply that ARMOR provides perfect securi- greater delays to travelers. Thus, assuming that all ty; in stead, ARMOR guarantees optimality in the check points and terminals are not being moni- utilization of fixed security resources (number of tored at all times, setting up available checkpoints, police or canine units) assuming the rewards are canine units, or other pa trols on deterministic accurately modeled. In other words, given a schedules allows adversaries to learn the schedules specific number of security resources and ar eas to and plot an attack that avoids the police check- protect, ARMOR creates a schedule that random- points and patrols, which makes deterministic izes over the possible deployment of those schedules in effective. resources in a fashion that optimizes the expected Randomization offers a solution here. In partic- reward obtained in protecting LAX. ular, from among all the security measures to The third challenge is addressed by ARMOR’s use which randomization could be applied, LAWA of a mixed-initiative-based interface, where users police have so far posed two cru cial problems to are allowed to graphically enter different con- us. First, given that there are many roads leading straints to shape the schedule generated. ARMOR is into LAX, they want to know where and when thus a collaborative assistant that it erates over gen- they should set up check points to check cars driv- erated schedules rather than a rigid one-shot ing into LAX. For example, figure 1 shows a vehic- scheduler. ARMOR also alerts users in case over- ular checkpoint set up on a road inbound towards rides may potentially deteriorate schedule quality. LAX. Police officers examine cars that drive by, ARMOR thus represents a very promising transi- and if any car appears suspicious, they do a more tion of multiagent research into a deployed appli- detailed inspec tion of that car. LAWA police cation. ARMOR has been successfully deployed wished to obtain a randomized schedule for such since August 2007 at the Los Angeles Internation- checkpoints for a particular time frame. For exam- al Airport (LAX) to assist the Los An geles World ple, if we are to set up two checkpoints, and the Airport (LAWA) police in randomized schedul ing timeframe of interest is 8 AM to 11 AM, then a can- of checkpoints and since November 2007 for gen- didate schedule may suggest to the police that on erating randomized patrolling schedules for canine Monday, check points should be placed on route 1 units. In particu lar, it assists police in determining and route 2, whereas on Tuesday during the same where to randomly set up checkpoints and where time slot, they should be on route 1 and 3, and so to randomly allocate canines to ter minals. Indeed, on. Second, LAWA police wished to obtain an February 2008 marked the successful end of the assignment of canines to patrol routes through the 44 AI MAGAZINE Articles terminals inside LAX. For example, if there are three canine units available, a possible assignment may be to place canines on terminals 1, 3, and 6 on the first day, but on terminals 2, 4, and 6 on another day, and so on based on the available in - formation. Figure 2 illustrates a canine unit on patrol at LAX.