Deployed ARMOR protection: the application of a game theoretic model for security at the Los Angeles International Airport

By James Adam Pita, Manish Jain, Janusz Marecki, Fernando Ordóñez, Christopher D. Portway, Milind Tambe, Craig Western, Praveen Paruchuri, Sarit Kraus. Adaptive Agents and Multi-Agent Systems, 2008

434 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0e54dff3873584983d5e10b8cc0cbdce65eb3482

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Citations in the corpus

From physical security to cybersecurity (2015, background): “Game-theoretic schedulers are seen to perform significantly better in providing higher levels of protections [1, 46].”
Protecting Moving Targets with Multiple Mobile Resources (2013, methodology): “(iv) Detailed experimental analysis of our algorithms in the ferry protection domain.1”
Computing optimal randomized resource allocations for massive security games (2009, methodology): “The most directly related work applies Stackelberg games to security patrolling, both in a generic “police and robbers” scenario [5] and a fielded application at the LAX airport [12].”
Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games (2008, methodology): “DOBSS thus represents a significant advance in the state of the art in addressing security domains and is at the heart of the ARMOR system that is currently being tested for security scheduling at the Los Angeles International Airport.”
Solving Partially Observable Stochastic Games with Public Observations (2019, methodology): “Game-theoretic strategies are used to protect critical infrastructures (Pita et al. 2008; Kiekintveld et al. 2009; Shieh et al. 2012), secure computer networks (Vanek et al. 2012; Nguyen, Wellman, and Singh 2017; Durkota et al. 2017) or wildlife (Fang, Stone, and Tambe 2015; Fang et al. 2016).”
Stackelberg vs. Nash in Security Games: An Extended Investigation of Interchangeability, Equivalence, and Uniqueness (2011, methodology): “For a particular game instance, we first compute an SSE strategy C using the DOBSS mixed-integer linear program (Pita et al., 2008).”
Deep Reinforcement Learning for Green Security Games with Real-Time Information (2018, background): “Most of the literature on SSG have neglected real-time information, with only a few exceptions (Zhang et al. 2014) that are not designed for green security domains.”