Computing the optimal strategy to commit to

Conitzer, Vincent; Sandholm, Tüomas

doi:10.1145/1134707.1134717

Cited by 355 publications

(397 citation statements)

References 17 publications

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“…Conitzer and Sandholm give complexity results and algorithms for computing optimal commitment strategies, including both pure and mixedstrategy commitments and a Bayesian case [4]. A new algorithm for solving Bayesian Stackelberg games (DOBSS) is central to the LAX application [11].…”

Section: Game-theoretic Modeling Of Security Gamesmentioning

confidence: 99%

“…In addition, the leader can often induce the favorable strong equilibrium by selecting a strategy arbitrarily close to the equilibrium that causes the the follower to strictly prefer the desired strategy [17]. We adopt strong Stackelberg equilibrium here due to the key existence result and because it is the most commonly adopted concept in the related literature [10,4,11 …”

Section: Stackelberg Equilibriummentioning

confidence: 99%

“…Coverage capabilities for each type are given by the function Ca : S × Ω → {0, 1}, which is 1 if the type is able to cover the given schedule and 0 otherwise. 4 The combination of schedules and resource types captures key elements of the FAMS domain. Suppose we model air marshals as resources, flights as targets, with payoffs defined by expert risk analysis.…”

Section: Scheduling and Resource Con-straintsmentioning

confidence: 99%

“…Our baseline for comparing with existing methods is DOBSS [11], which is the fastest known algorithm for general Bayesian Stackelberg games. While we do not consider the Bayesian case here, DOBSS is also comparable to other methods (notably [4]) for the non-Bayesian case. All of our algorithms generate optimal SSE solutions, so the primary metrics of comparison are the computational requirements to compute solutions, in both time and memory.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Computing Optimal Randomized Resource Allocations for Massive Security Games

Kiekintveld¹,

Jain²,

Tsai³

et al. 2011

Security and Game Theory

144

247

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Predictable allocations of security resources such as police officers, canine units, or checkpoints are vulnerable to exploitation by attackers. Recent work has applied game-theoretic methods to find optimal randomized security policies, including a fielded application at the Los Angeles International Airport (LAX). This approach has promising applications in many similar domains, including police patrolling for subway and bus systems, randomized baggage screening, and scheduling for the Federal Air Marshal Service (FAMS) on commercial flights. However, the existing methods scale poorly when the security policy requires coordination of many resources, which is central to many of these potential applications.We develop new models and algorithms that scale to much more complex instances of security games. The key idea is to use a compact model of security games, which allows exponential improvements in both memory and runtime relative to the best known algorithms for solving general Stackelberg games. We develop even faster algorithms for security games under payoff restrictions that are natural in many security domains. Finally, introduce additional realistic scheduling constraints while retaining comparable performance improvements. The empirical evaluation comprises both random data and realistic instances of the FAMS and LAX problems. Our new methods scale to problems several orders of magnitude larger than the fastest known algorithm.

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Section: Game-theoretic Modeling Of Security Gamesmentioning

confidence: 99%

Section: Stackelberg Equilibriummentioning

confidence: 99%

Section: Scheduling and Resource Con-straintsmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Computing Optimal Randomized Resource Allocations for Massive Security Games

Kiekintveld¹,

Jain²,

Tsai³

et al. 2011

Security and Game Theory

144

247

View full text Add to dashboard Cite

show abstract

“…I In a Bayesian Stackelberg game, the defender faces multiple types of adversaries, who might have different preference and objectives. Computing the optimal defender strategy for Bayesian Stackelberg games, so as to reach a "strong Stackelberg Equlibrium" is known to be a NP-hard problem [1].…”

Section: Securitymentioning

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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