2013

Albert Xin Jiang, Zhengyu Yin, Chao Zhang, Milind Tambe, and Sarit Kraus. 2013. “Game-theoretic Randomization for Security Patrolling with Dynamic Execution Uncertainty.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Abstract
In recent years there has been extensive research on game-theoretic models for infrastructure security. In time-critical domains where the security agency needs to execute complex patrols, execution uncertainty (interruptions) affect the patroller’s ability to carry out their planned schedules later. Indeed, experiments in this paper show that in some real-world domains, small fractions of execution uncertainty can have a dramatic impact. The contributions of this paper are threefold. First, we present a general Bayesian Stackelberg game model for security patrolling in dynamic uncertain domains, in which the uncertainty in the execution of patrols is represented using Markov Decision Processes. Second, we study the problem of computing Stackelberg equilibrium for this game. We show that when the utility functions have a certain separable structure, the defender’s strategy space can be compactly represented, and we can reduce the problem to a polynomial-sized optimization problem. Finally, we apply our approach to fare inspection in the Los Angeles Metro Rail system. Numerical experiments show that patrol schedules generated using our approach outperform schedules generated using a previous algorithm that does not consider execution uncertainty.
R. Yang, C. Kiekintvled, F. Ordonez, M. Tambe, and R. John. 2013. “Improving Resource Allocation Strategies Against Human Adversaries in Security Games: An Extended Study .” Artificial Intelligence Journal (AIJ), 195, Pp. 440-469.Abstract
Stackelberg games have garnered significant attention in recent years given their deployment for real world security. Most of these systems, such as ARMOR, IRIS and GUARDS have adopted the standard game-theoretical assumption that adversaries are perfectly rational, which is standard in the game theory literature. This assumption may not hold in real-world security problems due to the bounded rationality of human adversaries, which could potentially reduce the effectiveness of these systems. In this paper, we focus on relaxing the unrealistic assumption of perfectly rational adversary in Stackelberg security games. In particular, we present new mathematical models of human adversaries’ behavior, based on using two fundamental theory/method in human decision making: Prospect Theory (PT) and stochastic discrete choice model. We also provide methods for tuning the parameters of these new models. Additionally, we propose a modification of the standard quantal response based model inspired by rankdependent expected utility theory. We then develop efficient algorithms to compute the best response of the security forces when playing against the different models of adversaries. In order to evaluate the effectiveness of the new models, we conduct comprehensive experiments with human subjects using a web-based game, comparing them with models previously proposed in the literature to address the perfect rationality assumption on part of the adversary. Our experimental results show that the subjects’ responses follow the assumptions of our new models more closely than the previous perfect rationality assumption. We also show that the defender strategy produced by our new stochastic discrete choice model outperform the previous leading contender for relaxing the assumption of perfect rationality.Furthermore, in a separate set of experiments, we show the benefits of our modified stochastic model (QRRU) over the standard model (QR).
Jun-young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Yu-Han Chang, Milind Tambe, Burcin Becerik-Gerber, and Wendy Wood. 2013. “TESLA: An Energy-saving Agent that Leverages Schedule Flexibility .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Abstract
This innovative application paper presents TESLA, an agent-based application for optimizing the energy use in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. TESLA provides three key contributions: (i) three online scheduling algorithms that consider flexibility of people’s preferences for energyefficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; and (iii) surveys of real users that indicate that TESLA’s assumptions exist in practice. TESLA was evaluated on data of over 110,000 meetings held at nine campus buildings during eight months in 2011–2012 at USC and SMU. These results show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.
Jason Tsai, Yundi Qian, Yevgeniy Vorobeychik, Christopher Kiekintveld, and Milind Tambe. 2013. “Security Games with Contagion: Handling Asymmetric Information .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) [SHORT PAPER].Abstract
Counterinsurgency, which is the effort to mitigate support for an opposing organization, is one such domain that has been studied recently and past work has modeled the problem as an influence blocking maximization that features an influencer and a mitigator. While past work has introduced scalable heuristic techniques for generating effective strategies using a double oracle algorithm, it has not addressed the issue of uncertainty and asymmetric information, which is the topic of this paper.
Bo An, Matthew Brown, Yevgeniy Vorobeychik, and Milind Tambe. 2013. “Security Games with Surveillance Cost and Optimal Timing of Attack Execution.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).Abstract
Stackelberg games have been used in several deployed applications to allocate limited resources for critical infrastructure protection. These resource allocation strategies are randomized to prevent a strategic attacker from using surveillance to learn and exploit patterns in the allocation. Past work has typically assumed that the attacker has perfect knowledge of the defender’s randomized strategy or can learn the defender’s strategy after conducting a fixed period of surveillance. In consideration of surveillance cost, these assumptions are clearly simplistic since attackers may act with partial knowledge of the defender’s strategies and may dynamically decide whether to attack or conduct more surveillance. In this paper, we propose a natural model of limited surveillance in which the attacker dynamically determine a place to stop surveillance in consideration of his updated belief based on observed actions and surveillance cost. We show an upper bound on the maximum number of observations the attacker can make and show that the attacker’s optimal stopping problem can be formulated as a finite state space MDP. We give mathematical programs to compute optimal attacker and defender strategies. We compare our approaches with the best known previous solutions and experimental results show that the defender can achieve significant improvement in expected utility by taking the attacker’s optimal stopping decision into account, validating the motivation of our work.
2013. “Empirical Evaluation of Computational Fear Contagion Models in Crowd Dispersions .” Journal of Autonomous Agents and Multiagent Systems, JAAMAS, 27, 2, Pp. 200-217.Abstract
t In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions. While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents despite extensive evidence of its impacts in everyday life. In this paper, we examine computational models of emotional contagion by implementing two models ((Bosse et al, 2009b) and (Durupinar, 2010)) that draw from two separate lines of contagion research: thermodynamics-based and epidemiologicalbased. We first perform sensitivity tests on each model in an evacuation simulation, ESCAPES, showing both models to be reasonably robust to parameter variations with certain exceptions. We then compare their ability to reproduce a real crowd panic scene in simulation, showing that the thermodynamics-style model (Bosse et al, 2009b) produces superior results due to the ill-suited contagion mechanism at the core of epidemiological models. We also identify that a graduated effect of fear and proximity-based contagion effects are key to producing the superior results. We then reproduce the methodology on a second video, showing that the same results hold, implying generality of the conclusions reached in the first scene.

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