A new approach to the maximum-flow problem
Journal of the ACM (JACM)
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
Computing the optimal strategy to commit to
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
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
Settling the complexity of computing two-player Nash equilibria
Journal of the ACM (JACM)
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
The Complexity of Computing a Nash Equilibrium
SIAM Journal on Computing
Learning and Approximating the Optimal Strategy to Commit To
SAGT '09 Proceedings of the 2nd International Symposium on Algorithmic Game Theory
Quality-bounded solutions for finite Bayesian Stackelberg games: scaling up
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Journal of Artificial Intelligence Research
PROTECT: a deployed game theoretic system to protect the ports of the United States
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Reflections on Stanford's MOOCs
Communications of the ACM
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Conventionally, the questions on a test are assumed to be kept secret from test takers until the test. However, for tests that are taken on a large scale, particularly asynchronously, this is very hard to achieve. For example, example TOEFL iBT and driver's license test questions are easily found online. This also appears likely to become an issue for Massive Open Online Courses (MOOCs). In this paper, we take the loss of confidentiality as a fact. Even so, not all hope is lost as the test taker can memorize only a limited set of questions' answers, and the tester can randomize which questions appear on the test. We model this as a Stackelberg game, where the tester commits to a mixed strategy and the follower responds. We provide an exponential-size linear program formulation, prove several NP-hardness results, and give efficient algorithms for special cases.