{"type":"ScholarlyArticle","title":"Staying Ahead of the Game: Adaptive Robust Optimization for Dynamic Allocation of Threat Screening Resources","authors":[{"name":"Sara Marie Mc Carthy","slug":"sara-marie-mc-carthy","given":"Sara Marie Mc","family":"Carthy","affiliation":"University of Southern California"},{"name":"Phebe Vayanos","slug":"phebe-vayanos","given":"Phebe","family":"Vayanos","affiliation":"University of Southern California"},{"name":"Milind Tambe","slug":"milind-tambe","given":"Milind","family":"Tambe","affiliation":"University of Southern California"}],"abstract":"We consider the problem of dynamically allocating screening resources of different efficacies (e.g., magnetic or X-ray imaging) at checkpoints (e.g., at airports or ports) to successfully avert an attack by one of the screenees. Previously, the Threat Screening Game model was introduced to address this problem under the assumption that screenee arrival times are perfectly known. In reality, arrival times are uncertain, which severely impedes the implementability and performance of this approach. We thus propose a novel framework for dynamic allocation of threat screening resources that explicitly accounts for uncertainty in the screenee arrival times. We model the problem as a multistage robust optimization problem and propose a tractable solution approach using compact linear decision rules combined with robust reformulation and constraint randomization. We perform extensive numerical experiments which showcase that our approach outperforms (a) exact solution methods in terms of tractability, while incurring only a very minor loss in optimality, and (b) methods that ignore uncertainty in terms of both feasibility and optimality.","doi":"10.24963/ijcai.2017/527","url":"https://ijcai.org/proceedings/2017/527","pdf":"https://ijcai.org/proceedings/2017/0527.pdf","markdown":"https://ijcai.org/proceedings/2017/527.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/527","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":527,"pages":{"first":3770,"last":3776},"keywords":["Agent-based and Multi-agent Systems: Noncooperative Games","Knowledge Representation, Reasoning, and Logic: Game Theory","Multidisciplinary Topics and Applications: AI&Security and Privacy"],"videos":[],"references":[{"position":1,"raw":"[AAAE, 2014] AAAE. Transportation security policy. Tech- nical report, American Association of Airport Executives, 2014.","title":"AAAE. Transportation security policy","authors":"Aaae","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Balcan et al., 2015] Maria-Florina Balcan, Avrim Blum, Nika Haghtalab, and Ariel D Procaccia. Commitment without regrets: Online learning in stackelberg security games. In Proceedings of the Sixteenth ACM Confer- ence on Economics and Computation, pages 61-78. ACM, 2015.","title":"Commitment without regrets: Online learning in stackelberg security games","authors":"Maria-Florina Balcan, Avrim Blum, Nika Haghtalab, Ariel D Procaccia","year":"2015","venue":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Basilico et al., 2009] Nicola Basilico, Nicola Gatti, and Francesco Amigoni. Leader-follower strategies for robotic patrolling in environments with arbitrary topologies. In Proceedings of The 8th International Conference on Au- tonomous Agents and Multiagent Systems-Volume 1, pages 57-64. International Foundation for Autonomous Agents and Multiagent Systems, 2009.","title":"Leader-follower strategies for robotic patrolling in environments with arbitrary topologies","authors":"Nicola Basilico, Nicola Gatti, Francesco Amigoni","year":"2009","venue":"Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Ben-Tal et al., 2004] A. Ben-Tal, A. Goryashko, E. Gus- litzer, and A. Nemirovski. Adjustable robust solutions of uncertain linear programs. Mathematical Programming, 99(2):351-376, 2004.","title":"Adjustable robust solutions of uncertain linear programs","authors":"A Ben-Tal, A Goryashko, E Guslitzer, A Nemirovski","year":"2004","venue":"Mathematical Programming","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Birge and Louveaux, 1997] John R. Birge and Franois Lou- veaux. Introduction to stochastic programming. Springer series in operations research. Springer, New York, 1997.","title":"Introduction to stochastic programming","authors":"John R Birge, Franois Louveaux","year":"1997","venue":"Springer series in operations research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Brown et al., 2016] Matthew Brown, Arunesh Sinha, Aaron Schlenker, and Milind Tambe. One size does not fit all: A game-theoretic approach for dynamically and effectively screening for threats. In AAAI conference on Artificial In- telligence (AAAI), 2016.","title":"One size does not fit all: A game-theoretic approach for dynamically and effectively screening for threats","authors":"Matthew Brown, Arunesh Sinha, Aaron Schlenker, Milind Tambe","year":"2016","venue":"AAAI conference on Artificial Intelligence (AAAI)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Campi and Garatti, 2008] Marco C Campi and Simone Garatti. The exact feasibility of randomized solutions of uncertain convex programs. SIAM Journal on Optimiza- tion, 19(3):1211-1230, 2008.","title":"The exact feasibility of randomized solutions of uncertain convex programs","authors":"C Marco, Simone Campi, Garatti","year":"2008","venue":"SIAM Journal on Optimization","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fischetti and Monaci, 2012] Matteo Fischetti and Michele Monaci. Cutting plane versus compact formulations for uncertain (integer) linear programs. Mathematical Pro- gramming Computation, 4(3):239-273, 2012.","title":"Cutting plane versus compact formulations for uncertain (integer) linear programs","authors":"Matteo Fischetti, Michele Monaci","year":"2012","venue":"Mathematical Programming Computation","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jain et al., 2012] Manish Jain, Kevin Leyton-Brown, and Milind Tambe. The deployment-to-saturation ratio in se- curity games. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pages 1362-1370. AAAI Press, 2012.","title":"The deployment-to-saturation ratio in security games","authors":"Manish Jain, Kevin Leyton-Brown, Milind Tambe","year":"2012","venue":"Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Korzhyk et al., 2010] D. Korzhyk, V. Conitzer, and R. Parr. Complexity of computing optimal Stackelberg strategies in security resource allocation games. In Proceedings of the 24th AAAI conference on Artificial Intelligence (AAAI), pages 805-810, 2010.","title":"Complexity of computing optimal Stackelberg strategies in security resource allocation games","authors":"D Korzhyk, V Conitzer, R Parr","year":"2010","venue":"Proceedings of the 24th AAAI conference on Artificial Intelligence (AAAI)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Schlenker et al., 2016] Aaron Schlenker, Matthew Brown, Arunesh Sinha, and Milind Tambe. Get me to my gate on time: Efficiently solving general-sum bayesian threat screening games. In European Conference on AI (ECAI), 2016.","title":"Get me to my gate on time: Efficiently solving general-sum bayesian threat screening games","authors":"Aaron Schlenker, Matthew Brown, Arunesh Sinha, Milind Tambe","year":"2016","venue":"European Conference on AI (ECAI)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Tambe, 2011] Milind Tambe. Security and game theory: al- gorithms, deployed systems, lessons learned. Cambridge University Press, 2011.","title":"Security and game theory: algorithms, deployed systems, lessons learned","authors":"Milind Tambe","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Vayanos et al., 2012] Phebe Vayanos, Daniel Kuhn, and Berc ¸Rustem. A constraint sampling approach for multi- stage robust optimization. Automatica, 48(3):459-471, 2012.","title":"A constraint sampling approach for multistage robust optimization","authors":"Vayanos","year":"2012","venue":"Automatica","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Yin et al., 2015] Yue Yin, Haifeng Xu, Jiarui Gain, Bo An, and Albert Xin Jiang. Computing optimal mixed strategies for security games with dynamic payoffs. In Proceedings of the 24th International Conference on Artificial Intelli- gence, pages 681-687. AAAI Press, 2015.","title":"Computing optimal mixed strategies for security games with dynamic payoffs","authors":"Haifeng Yue Yin, Jiarui Xu, Bo Gain, Albert Xin An, Jiang","year":"2015","venue":"Proceedings of the 24th International Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/102"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}