Software Assistants for Randomized Patrol Planning for the LAX Airport Police and the Federal Air Marshal Service

Jain, Manish; Tsai, Jason; Pita, James; Kiekintveld, Christopher; Rathi, Shyamsunder; Tambe, Milind; Ordóòez, Fernando

doi:10.1287/inte.1100.0505

Cited by 166 publications

(139 citation statements)

References 29 publications

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“…Security games, an important class of attackerdefender Stackelberg games, are at the heart of several significant deployed decision-support applications. Such systems include ARMOR at the Los Angeles International Airport (LAX) [20], IRIS deployed by the US Federal Air Marshals Service [20], GUARDS developed for the US Transportation Security Administration [3], and PROTECT used at the Port of Boston by the US Coast Guard [3].…”

Section: Introductionmentioning

confidence: 99%

An extended study on multi-objective security games

Brown

Kiekintveld

et al. 2012

Auton Agent Multi-Agent Syst

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The burgeoning area of security games has focused on real-world domains where security agencies protect critical infrastructure from a diverse set of adaptive adversaries. In such domains, decision makers have multiple competing objectives they must consider which may take different forms that are not readily comparable including safety, cost, and public perception. Thus, it can be difficult to know how to weigh the different objectives when deciding on a security strategy. To address the challenges of these domains, we propose a fundamentally different solution concept, multi-objective security games (MOSG). Instead of a single optimal solution, MOSGs have a set of Pareto optimal (non-dominated) solutions referred to as the Pareto frontier, which can be generated by solving a sequence of constrained single-objective optimization problems (CSOP). The Pareto frontier allows the decision maker to analyze the tradeoffs that exist between the multiple objectives. Our contributions include: (i) an algorithm, Iterative--Constraints, for generating the sequence of CSOPs; (ii) an exact approach for solving an MILP formulation of a CSOP; (iii) heuristics that achieve speed up by exploiting the structure of security games to further constrain the MILP; (iv) an approximate approach for solving a CSOP built off those same heuristics, increasing the scalability of our approach with quality guarantees. Additional contributions of this paper include proofs on the level of approximation, detailed experimental evaluation of the proposed approaches and heuristics, as well as a discussion on techniques for visualizing the Pareto frontier.

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Section: Introductionmentioning

confidence: 99%

An extended study on multi-objective security games

Brown

Kiekintveld

et al. 2012

Auton Agent Multi-Agent Syst

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“…The payoffs for the attacker are in the same format, U e., when a defender covers b, she receives a higher reward while the attacker receives a lower reward compared to when the defender does not cover b [5,28,56]. The model in our paper allows a non-zero-sum game, where the sum of the defender's and attacker's payoff values may be non-zero.…”

Section: Security Games With An Example Application In the Metro Domainmentioning

confidence: 99%

“…Significant research on the strong Stackelberg equilibrium versus other types of Stackelberg equilibrium has already been done in previous work and led to SSE being commonly used in security game research [3,12,19,26,27,28,30,31,44,45,49,50,66].…”

Section: Security Games With An Example Application In the Metro Domainmentioning

confidence: 99%

An extended study on addressing defender teamwork while accounting for uncertainty in attacker defender games using iterative Dec-MDPs

Shieh

Jiang

Yadav

et al. 2016

MGS

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Multi-agent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multi-agent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork 1 research in security games. The problem that this paper seeks to solve is the coordination of decentralized defender agents in the presence of uncertainty while securing targets against an observing adversary. To address this problem, we offer the following novel contributions in this paper: (i) New model of security games with defender teams that coordinate under uncertainty; (ii) New algorithm based on column generation that utilizes Decentralized Markov Decision Processes (Dec-MDPs) to generate defender strategies that incorporate uncertainty; (iii) New techniques to handle global events (when one or more agents may leave the system) during defender execution; (iv) Heuristics that help scale up in the number of targets and agents to handle real-world scenarios; (v) Exploration of the robustness of randomized pure strategies. The paper opens the door to a potentially new area combining computational game theory and multi-agent teamwork.

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“…In particular, we developed: (i) Aspen and Rugged, algorithms that compute the optimal defender strategy with a very large number of pure strategies for both the defender and the attacker [3,5]; (ii) a new hierarchical framework for Bayesian games that can scale-up to large number of attacker types and is applicable to all Stackelberg solvers [4]. Moreover, these algorithms have not only been experimentally validated, but Aspen has also been deployed in the real-world [6].…”

Section: Addressing the Scalability Challengementioning

confidence: 99%

Game Theory and Human Behavior: Challenges in Security and Sustainability

Rong

Tambe

Jain

et al. 2011

Algorithmic Decision Theory

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Abstract. Security and sustainability are two critical global challenges that involve the interaction of many intelligent actors. Game theory provides a sound mathematical framework to model such interactions, and computational game theory in particular has a promising role to play in helping to address key aspects of these challenges. Indeed, in the domain of security, we have already taken some encouraging steps by successfully applying game-theoretic algorithms to real-world security problems: our algorithms are in use by agencies such as the US coast guard, the Federal Air Marshals Service, the LAX police and the Transportation Security Administration. While these applications of game-theoretic algorithms have advanced the state of the art, this paper lays out some key challenges as we continue to expand the use of these algorithms in real-world domains. One such challenge in particular is that classical game theory makes a set of assumptions of the players, which may not be consistent with real-world scenarios, especially when humans are involved. To actually model human behavior within game-theoretic framework, it is important to address the new challenges that arise due to the presence of human players: (i) human bounded rationality; (ii) limited observations and imperfect strategy execution; (iii) large action spaces. We present initial solutions to these challenges in context of security games. For sustainability, we lay out our initial efforts and plans, and key challenges related to human behavior in the loop.

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Software Assistants for Randomized Patrol Planning for the LAX Airport Police and the Federal Air Marshal Service

Cited by 166 publications

References 29 publications

An extended study on multi-objective security games

An extended study on multi-objective security games

An extended study on addressing defender teamwork while accounting for uncertainty in attacker defender games using iterative Dec-MDPs

Game Theory and Human Behavior: Challenges in Security and Sustainability

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