Mathematical Programming: Series A and B
Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Simultaneously modeling humans' preferences and their beliefs about others' preferences
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track
Leader-follower strategies for robotic patrolling in environments with arbitrary topologies
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Game Theoretical Insights in Strategic Patrolling: Model and Algorithm in Normal-Form
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Modeling reciprocal behavior in human bilateral negotiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Adversarial uncertainty in multi-robot patrol
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
GUARDS and PROTECT: next generation applications of security games
ACM SIGecom Exchanges
GUARDS: game theoretic security allocation on a national scale
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
A study of computational and human strategies in revelation games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
The effect of expression of anger and happiness in computer agents on negotiations with humans
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Journal of Artificial Intelligence Research
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Security games with multiple attacker resources
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Improving resource allocation strategy against human adversaries in security games
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
An ambiguity aversion framework of security games under ambiguities
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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 rank-dependent 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).