Security Games with Arbitrary Schedules: A Branch-and-Price Approach

Lecture Notes in Computer Science

2014

Self Cite

Abstract. Boundedly rational human adversaries pose a serious challenge to security because they deviate from the classical assumption of perfect rationality. An emerging trend in security game research addresses this challenge by using behavioral models such as quantal response (QR) and subjective utility quantal response (SUQR). These models improve the quality of the defender's strategy by more accurately modeling the decisions made by real human adversaries. Work on incorporating human behavioral models into security games has typically followed two threads. The first thread, scalability, seeks to develop efficient algorithms to design patrols for large-scale domains that protect against a single adversary. However, this thread cannot handle the common situation of multiple adversary types with heterogeneous behavioral models. Having multiple adversary types introduces considerable uncertainty into the defender's planning problem. The second thread, robustness, uses either Bayesian or maximin approaches to handle this uncertainty caused by multiple adversary types. However, the robust approach has so far not been able to scale up to complex, large-scale security games. Thus, each of these two threads alone fails to work in key real world security games. Our present work addresses this shortcoming and merges these two research threads to yield a scalable and robust algorithm, MIDAS (MaxImin Defense Against SUQR), for generating game-theoretic patrols to defend against multiple boundedly rational human adversaries. Given the size of the defender's optimization problem, the key component of MIDAS is incremental cut and strategy generation using a master/slave optimization approach. Innovations in MIDAS include (i) a maximin mixed-integer linear programming formulation in the master and (ii) a compact transition graph formulation in the slave. Additionally, we provide a theoretical analysis of our new model and report its performance in simulations. In collaboration with the United States Coast Guard (USCG), we consider the problem of defending fishery stocks from illegal fishing in the Gulf of Mexico and use MIDAS to handle heterogeneity in adversary types (i.e., illegal fishermen) in order to construct robust patrol strategies for USCG assets.

Section: Column Generationmentioning

confidence: 99%

“…Specifically, we solve Problem (28) using the technique in [6], i.e. we use a maximum reward network flow problem (since Problem (28) is a maximization problem).…”

Section: Column Generationmentioning

confidence: 99%

Addressing Scalability and Robustness in Security Games with Multiple Boundedly Rational Adversaries

Brown

Haskell

Lecture Notes in Computer Science

2014

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“…At its backend, IRIS casts the problem it solves as a Stackelberg game and in particular as a security game with a special payoff structure. IRIS uses the Aspen algorithm [3], and is in use by FAMS since 2009.…”

Section: Securitymentioning

confidence: 99%

“…We provide new models and algorithms that compute optimal defender strategies for massive real-world security domains. 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

Algorithmic Decision Theory

Jain

et al. 2011

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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.

“…The second issue is in efficiently solving the model we developed where, because we consider a national deployment, a specialpurpose solver may not be appropriate. In fact, previous solution techniques [8,9] for traditional security games are no longer directly applicable. The final issue is in knowledge acquisition for the many variables involved in TSA's security challenges.…”

Section: Introductionmentioning

confidence: 99%

GUARDS: Game-Theoretic Security Allocation on a National Scale

Pita¹,

Security and Game Theory

Kiekintveld³

et al. 2011

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Building on research previously reported at AAMAS conferences, this paper describes an innovative application of a novel gametheoretic approach for a national scale security deployment. Working with the United States Transportation Security Administration (TSA), we have developed a new application called GUARDS to assist in resource allocation tasks for airport protection at over 400 United States airports. In contrast with previous efforts such as AR-MOR and IRIS, which focused on one-off tailored applications and one security activity (e.g. canine patrol or checkpoints) per application, GUARDS faces three key issues: (i) reasoning about hundreds of heterogeneous security activities; (ii) reasoning over diverse potential threats; (iii) developing a system designed for hundreds of end-users. Since a national deployment precludes tailoring to specific airports, our key ideas are: (i) creating a new game-theoretic framework that allows for heterogeneous defender activities and compact modeling of a large number of threats; (ii) developing an efficient solution technique based on general purpose Stackelberg game solvers; (iii) taking a partially centralized approach for knowledge acquisition and development of the system. In doing so we develop a software scheduling assistant, GUARDS, designed to reason over two agents -the TSA and a potential adversaryand allocate the TSA's limited resources across hundreds of security activities in order to provide protection within airports.The scheduling assistant has been delivered to the TSA and is currently under evaluation and testing for scheduling practices at an undisclosed airport. If successful, the TSA intends to incorporate the system into their unpredictable scheduling practices nationwide. In this paper we discuss the design choices and challenges encountered during the implementation of GUARDS. GUARDS represents promising potential for transitioning years of academic research into a nationally deployed system.