featuring Milind Tambe
University of Southern California
hosted by Subarao Kambhampati
Thursday, February 21st, 2013, 1:30 p.m. – 2:30 p.m.
“Security and Game Theory: Key Algorithmic Principles, Deployed Applications, Lessons Learned”
Abstract: Security is a critical concern around the world, whether it is the challenge of protecting ports, airports and other critical national infrastructure, or protecting wildlife/forests and fish, or suppressing crime in urban areas. In many of these cases, limited security resourcesprevent full security coverage at all times. Instead, these limited resources must be allocated and scheduled efficiently, avoiding schedule predictability, while simultaneously taking into account an adversary’s response to the security coverage, the adversary’s preferences and potential uncertainty over such preferences and adversary’s capabilities.
Computational game theory can help us build decision-aids for such efficient security resource allocation. Indeed, casting the security allocation problem as a Bayesian Stackelberg game, we have developed new algorithms that are deployed over multiple years in multiple applications: for the US coast guard in Boston, New York and Los Angeles (and now getting deployed at other ports), for the Federal Air Marshals(FAMS), for the Los Angeles Airport Police, with the Los Angeles Sheriff’s Department, with further applications under evaluation for the TSA and other agencies. These applications are leading to real-world use-inspired research in the emerging research area of “security games”: these research challenges include scaling up security games to large-scale problems, handling significant adversarial uncertainty, dealing with bounded rationality of human adversaries, and other interdisciplinary challenges. I will provide an overview of my research’s group’s work in this area, outlining key algorithmic principles, research results, as well as a discussion of our deployed systems and lessons learned.
(*) This is joint work with a number of former and current PHD students, postdocs, and other collaborators, all listed at: http://teamcore.usc.edu/security
Bio: Milind Tambe is a Helen N. and Emmett H. Jones Professor in Engineering at the University of Southern California(USC). He is a fellow of AAAI, recipient of the ACM/SIGART Autonomous Agents Research Award, as well as recipient of the Christopher Columbus Fellowship Foundation Homeland security award. In addition, he is also the recipient of the “influential paper award” from the International Foundation for Agents and Multiagent Systems, the INFORMS Wagner prize for excellence in Operations Research practice, the Rist Prize of the Military Operations Research Society, US Coast Guard First District’s Operational Excellence Award, Certificate of Appreciation from the US Federal Air Marshals Service, special commendation given by the Los Angeles World Airports police from the city of Los Angeles, IBM Faculty Award, Okawa foundation faculty research award, the RoboCup scientific challenge award, USC Viterbi School of Engineering use-inspired research award, USC Steven B. Sample Teaching and Mentoring award and the ACM recognition of service award. Prof. Tambe and his research group’s papers have been selected as best papers at a dozen premier Artificial Intelligence Conferences and workshops; these have included best paper awards at the International Conference on Autonomous Agents and Multiagent Systems and International Conference on Intelligent Virtual Agents. Additionally, the “security games” algorithms pioneered by his Teamcore research group have been deployed for real-world use by several security agencies including the US Coast Guard, the US Federal Air Marshals service, LAX Police and the LA Sheriff’s Department; this research has also recently led to his co-founding ARMORWAY, a security resource optimization company, where he serves on the board of directors. Prof. Tambe received his Ph.D. from the School of Computer Science at Carnegie Mellon University.
Questions? Contact Subbarao Kambhampati at email@example.com