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 coverage at all times. Furthermore, adversaries can observe security 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, randomized patrolling is important in tasks ranging from security 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 challenges in building such a software assistant. First, the assistant 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 attack 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 enable 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 assistant agent called ARMOR (assistant for randomized monitoring over routes). Based on game-theoretic principles, ARMOR combines three key features to address each of the challenges outlined above. Game theory is a well-established foundational principle within multiagent systems to reason about multiple agents, each pursuing its own interests (Fudenberg and Tirole 1991). We build on these game-theoretic foundations to reason about two agents--the police force and its adversary--in providing a method of randomization. In particular, the main contribution of our article is mapping the problem of security scheduling as a Bayesian Stackelberg game (Conitzer and Sandholm 2006) and solving it through the fastest optimal algorithm for such games (Paruchuri et al. 2008), addressing the first two challenges. The algorithm used builds on several years of research regarding multiagent systems and security (Paruchuri et al. 2005, 2006, 2007). In particular, ARMOR relies on an optimal algorithm called DOBSS (decomposed optimal Bayesian Stackelberg solver) (Paruchuri et al. 2008).
While a Bayesian game allows us to address uncertainty over adversary types, by optimally solving such Bayesian Stackelberg games (which yield optimal randomized strategies as solutions), ARMOR provides quality guarantees on the schedules generated. These quality guarantees obviously do not imply that ARMOR provides perfect security; instead, ARMOR guarantees optimality in the utilization of fixed security resources (number of police or canine units) assuming the rewards are accurately modeled. In other words, given a specific number of security resources and areas to protect, ARMOR creates a schedule that randomizes over the possible deployment of those resources in a fashion that optimizes the expected reward obtained in protecting LAX.
The third challenge is addressed by ARMOR's use of a mixed-initiative-based interface, where users are allowed to graphically enter different constraints to shape the schedule generated. …