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. Casting the problem as a Bayesian Stackelberg game, I have developed new algorithms for efficiently solving such games to provide randomized patrolling or inspection strategies: my algorithms address scale-up in these security scheduling problems avoiding predictability, addressing key weaknesses of human scheduling. These algorithms are now deployed in multiple applications. ARMOR, the first game theoretic application, has been deployed at the Los Angeles International Airport (LAX) since 2007 to randomizes checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals. IRIS, the second application, is a game-theoretic scheduler for randomized deployment of the Federal Air Marshals (FAMS) requiring significant scale-up in underlying algorithms; IRIS has been in use since 2009. Furthermore, the success of IRIS and ARMOR systems has led to newer deployments of such algorithms in other real-world security domains, like the PROTECT system for the U.S. Coast Guard, and the TRUSTS system for the LA Sheriff Department. These applications are leading to real-world use-inspired research in scaling up to large-scale problems, handling significant adversarial uncertainty, dealing with bounded rationality of human adversaries, and other fundamental challenges. This talk will outline the research in the domain of security applications, with a focus on my contributions.
Manish Jain is currently a PhD candidate at the University of Southern California. He is a part of the Teamcore Research group, led by Prof. Milind Tambe. His work is on the applications of game-theoretic and large-scale optimization techniques, including the scheduling of flights/air marshals for the Federal Air Marshals Service (FAMS) and the scheduling of checkpoints for the Los Angeles International Airport (LAX) police. He has co-authored papers on the subject of security games that have been presented in major artificial intelligence and operations research conferences. His work published in Interfaces was a finalist for the EURO excellence in Practice award. He has also received a Letter of Commendation from the FAMS and the city of Los Angeles for his contributions to the development of the security assistants deployed at LAX and with the FAMS.