The human element: addressing human adversaries in security domains

Recently, game theory has been shown to be useful for reasoning about real-world security settings where security forces must protect critical assets from potential adversaries. In fact, there have been a number of deployed real-world applications of game theory for security (e.g., ARMOR at Los Angeles International Airport and IRIS for the Federal Air Marshals Service). Here, the objective is for the security force to utilize its limited resources to best defend their critical assets. An important factor in these real-world security settings is that the adversaries involved are humans who may not behave according to the standard assumptions of game-theoretic models. There are two key shortcomings of the approaches currently employed in these recent applications. First, human adversaries may not make the predicted rational decision. In such situations, where the security force has optimized against a perfectly rational opponent, a deviation by the human adversary can lead to adverse affects on the security force's predicted outcome. Second, human adversaries are naturally creative and security domains are highly dynamic, making enumeration of all potential threats a practically impossible task and solving the resulting game, with current leading approaches, would be intractable. My thesis contributes to a very new area that combines algorithmic and experimental game theory. Indeed, it examines a critical problem in applying game-theoretic techniques to situations where perfectly rational solvers must address human adversaries. In doing so it advances the study and reach of game theory to domains where software agents and humans may interact. More specifically, to address the first shortcoming, my thesis presents two separate algorithms to address potential deviations from the predicted rational decision by human adversaries. Experimental results, from a simulation that is motivated by a real-world security domain at Los Angeles International airport, demonstrated that both of my approaches outperform the currently deployed optimal algorithms which utilize standard game-theoretic assumptions and additional alternative algorithms against humans. In fact, one of my approaches is currently under evaluation in a real-world application to aid in resource allocation decisions for the United States Coast Guard. Towards addressing the second shortcoming of enumeration of a large number of potential adversary threat capabilities, I introduce a new game-theoretic model for efficiency, which additionally generalizes the previously accepted model for security domains. This new game-theoretic model for addressing human threat capabilities has seen real-world deployment and is under evaluation to aid the United States Transportation Security Administration in their resource allocation challenges.