Sequential Stackelberg equilibria in two-person games
Journal of Optimization Theory and Applications
Achieving network optima using Stackelberg routing strategies
IEEE/ACM Transactions on Networking (TON)
Rank-score tests in factorial designs with repeated measures
Journal of Multivariate Analysis
Mathematical Programming: Series A and B
Computing the optimal strategy to commit to
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Security in multiagent systems by policy randomization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Predicting people's bidding behavior in negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Defending Critical Infrastructure
Interfaces
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
The impact of adversarial knowledge on adversarial planning in perimeter patrol
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
Effective solutions for real-world Stackelberg games: when agents must deal with human uncertainties
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Facing the challenge of human-agent negotiations via effective general opponent modeling
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
Adversarial uncertainty in multi-robot patrol
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Stackelberg vs. Nash in security games: interchangeability, equivalence, and uniqueness
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
GUARDS and PROTECT: next generation applications of security games
ACM SIGecom Exchanges
Improved computational models of human behavior in security games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Real-world security games: toward addressing human decision-making uncertainty
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Game theory and human behavior: challenges in security and sustainability
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
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
A robust approach to addressing human adversaries in security games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Game theory for security: an important challenge for multiagent systems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Security games with surveillance cost and optimal timing of attack execution
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Modeling human adversary decision making in security games: an initial report
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Computing Stackelberg strategies in stochastic games
ACM SIGecom Exchanges
Scaling-up security games with boundedly rational adversaries: a cutting-plane approach
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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How do we build algorithms for agent interactions with human adversaries? Stackelberg games are natural models for many important applications that involve human interaction, such as oligopolistic markets and security domains. In Stackelberg games, one player, the leader, commits to a strategy and the follower makes her decision with knowledge of the leader's commitment. Existing algorithms for Stackelberg games efficiently find optimal solutions (leader strategy), but they critically assume that the follower plays optimally. Unfortunately, in many applications, agents face human followers (adversaries) who - because of their bounded rationality and limited observation of the leader strategy - may deviate from their expected optimal response. In other words, human adversaries' decisions are biased due to their bounded rationality and limited observations. Not taking into account these likely deviations when dealing with human adversaries may cause an unacceptable degradation in the leader's reward, particularly in security applications where these algorithms have seen deployment. The objective of this paper therefore is to investigate how to build algorithms for agent interactions with human adversaries. To address this crucial problem, this paper introduces a new mixed-integer linear program (MILP) for Stackelberg games to consider human adversaries, incorporating: (i) novel anchoring theories on human perception of probability distributions and (ii) robustness approaches for MILPs to address human imprecision. Since this new approach considers human adversaries, traditional proofs of correctness or optimality are insufficient; instead, it is necessary to rely on empirical validation. To that end, this paper considers four settings based on real deployed security systems at Los Angeles International Airport (Pita et al., 2008 [35]), and compares 6 different approaches (three based on our new approach and three previous approaches), in 4 different observability conditions, involving 218 human subjects playing 2960 games in total. The final conclusion is that a model which incorporates both the ideas of robustness and anchoring achieves statistically significant higher rewards and also maintains equivalent or faster solution speeds compared to existing approaches.