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Game Theory for Security: Lessons learned from deployed applications

Game Theory for Security: Lessons learned from deployed applications

This video was recorded at 27th Conference on Uncertainty in Artificial Intelligence (UAI), Barcelona 2011. Security at infrastructure of economic, political, or symbolic importance is a key concern around the world. Game theory is well-suited to adversarial reasoning for security resource allocation and scheduling problems, and allows us to generate security strategies that are unpredictable, but also based on the information about the relative risks of different kinds of attacks. Many new algorithms and modeling techniques have been developed to support recent real-world applications of game-theoretic analysis in security domains. For instance, the ARMOR system was the first to apply this framework, and has been deployed at the Los Angeles International Airport (LAX) since August 2007 to randomize checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals. The IRIS tool was developed as a game-theoretic scheduler for randomized deployment of the Federal Air Marshals (FAMS). These applications are leading to use-inspired research in scaling up to very compels problems, handling significant adversarial uncertainty, dealing with bounded rationality of human adversaries, and other fundamental challenges. This tutorial will cover: (i) background on game theory and basic adversarial reasoning techniques; (ii) overview of some fielded applications and a discussion of the key challenges; (iii) an in-depth discussion of the basic algorithmic approaches to solving these games and a brief overview of more advanced techniques and recent results; (iv) discussion of the challenge of modeling different types of uncertainty in security domains and solving the resulting game formulations; and (v) presentation of methods used in evaluation of this research.


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