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AIC Seminar Series

Game Theory for Security: Lessons learned from deployed applications

Milind TambeUniversity of Southern California[Home Page]

Notice:  Hosted by Karen Myers. Note the non-usual weekday and time.

Date:  Tuesday, June 1st 2010 at 10:30am

Location:  EJ228 (SRI E building); slides via WebEx from 10:15am on, sound at 1-888-355-1249, 749045  (Directions)


Security at major locations of economic or political importance or transportation or other infrastructure is a key concern around the world, particularly given the threat of terrorism. Limited security resources prevent full security coverage at all times; instead, these limited resources must be deployed intelligently taking into account differences in priorities of targets requiring security coverage, the responses of the adversaries to the security posture and potential uncertainty over the types of adversaries faced.

Game theory is well-suited to adversarial reasoning for security resource allocation and scheduling problems because it suggests randomized policies that mitigate a key vulnerability of human plans: predictability. Casting the problem as a Bayesian Stackelberg game, we have developed new algorithms for efficiently solving such games to provide randomized patrolling or inspection strategies; our algorithms are now deployed in multiple applications. ARMOR (Assistant for Randomized Monitoring over Routes), our first game theoretic application, has been deployed at the Los Angeles International Airport (LAX) since August 2007 to randomizes checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals. IRIS, our second application, is a game-theoretic scheduler for randomized deployment of the Federal Air Marshals (FAMS) requiring significant scale-up in underlying algorithms; IRIS was put into use to generate schedules in late 2009. Finally, GUARDS has been deployed by the TSA (Transportation Security Administration) at the Pittsburgh and LAX airports starting October 2009 for pilot evaluation with a goal of large-scale deployments across multiple airports. These applications are leading to real-world use-inspired research. This talk will outline our algorithms (often based on techniques borrowed from operations research), key research results and lessons learned.

(*) This is joint work with my PhD students (Manish Jain, James Pita, Jason Tsai, Zhengyu Yin, Praveen Paruchuri), postdoctoral researchers (Chris Kiekintveld, Erim Kardes), key faculty collaborators at USC(Fernando Ordonez, David Kempe) and at other universities (Sarit Kraus, Vince Conitzer), and a team of undergraduate and MS students.

   Bio for Milind Tambe

Milind Tambe is a Professor of Computer Science and Industrial and Systems Engineering at the University of Southern California (USC). He received his Ph.D. from the School of Computer Science at Carnegie Mellon University. He leads the TEAMCORE Research Group at USC, with research is focused on agent-based and multi-agent systems. He is a fellow of AAAI (Association for Advancement of Artificial Intelligence) and recipient of the ACM (Association for Computing Machinery) SIGART Agents Research award. He is also the recipient of a special commendation given by the Los Angeles World Airports police from the city of Los Angeles, USC Viterbi School of Engineering use-inspired research award, Okawa foundation faculty research award, the RoboCup scientific challenge award, and the ACM recognition of service award.

Prof. Tambe and his research group’s papers have been selected as best papers or finalists for best papers at a dozen premier Artificial Intelligence and Operations Research Conferences and workshops, and their algorithms have been deployed for real-world use by several agencies including the LAX police, the Federal Air Marshals service and the Transportation security administration.

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