Security at major locations of economic or political importance is a key concern around the world, particularly given the threat of terrorism. Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or monitoring; for example, they can plan an attack avoiding existing patrols. Hence, randomized patrolling or monitoring is important, but randomization must provide distinct weights to different actions based on their complex costs and benefits. To this end, this article describes a promising transition of the latest in multiagent algorithms into a deployed application. In particular, it describes a software assistant agent called ARMOR (assistant for randomized monitoring over routes) that casts this patrolling and monitoring problem as a Bayesian Stackelberg game, allowing the agent to appropriately weigh the different actions in randomization, as well as uncertainty over adversary types. ARMOR combines two key features. It uses the fastest known solver for Bayesian Stackelberg games called DOBSS, where the dominant mixed strategies enable randomization; and its mixed-initiative-based interface allows users occasionally to adjust or override the automated schedule based on their local constraints. ARMOR has been successfully deployed since August 2007 at the Los Angeles International Airport (LAX) to randomize checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals. This article examines the information, design choices, challenges, and evaluation that went into designing ARMOR.
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Protecting national infrastructure such as airports, historical landmarks, or a location of political or economic importance is a challenging task for police and security agencies around the world, a challenge that is exacerbated by the threat of terrorism. Such protection of important locations includes tasks such as monitoring all entrances or inbound roads and checking inbound traffic. However, limited resources imply that it is typically impossible to provide full security cover age at all times. Furthermore, adversaries can observe se curity arrangements over time and exploit any predictable patterns to their advantage. Randomizing schedules for patrolling, checking, or monitoring is thus an important tool in the police arsenal to avoid the vulnerability that comes with predictability. Even beyond protecting infrastructure, ran domized patrolling is important in tasks ranging from secu rity on university campuses to normal police beats to border or maritime security (Billante 2003, Paruchuri et al. 2007, Ruan et al. 2005).
This article focuses on a deployed software assistant agent that can aid police or other security agencies in randomizing their security schedules. We face at least three key chal lenges in building such a software assistant. First, the as sistant must provide quality guarantees in randomization by appropriately weighing the costs and benefits of the different options available. For example, if an attack on one part of an infrastructure will cause economic damage while an at tack on another could potentially cost human lives, we must weigh the two options differently-giving higher weight (probability) to guarding the latter. Second, the assistant must address the uncertainty in information that security forces have about the adversary. Third, the assistant must en able a mixed-initiative interaction with potential users rather than dictate a schedule; the assistant may be unaware of users' real-world constraints, and hence users must be able to shape the schedule development.
We have addressed these challenges in a software assis tant agent called ARMOR (assistant for randomized mon itoring over routes). Based on game-theoretic principles, ARMOR combines three key features to address each of the challenges outlined above. …