Disjunctive programming: properties of the convex hull of feasible points
Discrete Applied Mathematics
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
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
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
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
A graph-theoretic approach to protect static and moving targets from adversaries
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
GUARDS: game theoretic security allocation on a national scale
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Quality-bounded solutions for finite Bayesian Stackelberg games: scaling up
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Information-theoretic approaches to branching in search
Discrete Optimization
Monte Carlo bounding techniques for determining solution quality in stochastic programs
Operations Research Letters
Game-theoretic randomization for security patrolling with dynamic execution uncertainty
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Security games with interval uncertainty
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Security games with contagion: handling asymmetric information
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Planning and learning in security games
ACM SIGecom Exchanges
Hi-index | 0.00 |
Given their existing and potential real-world security applications, Bayesian Stackelberg games have received significant research interest [3, 12, 8]. In these games, the defender acts as a leader, and the many different follower types model the uncertainty over discrete attacker types. Unfortunately since solving such games is an NP-hard problem, scale-up has remained a difficult challenge. This paper scales up Bayesian Stackelberg games, providing a novel unified approach to handling uncertainty not only over discrete follower types but also other key continuously distributed real world uncertainty, due to the leader's execution error, the follower's observation error, and continuous payoff uncertainty. To that end, this paper provides contributions in two parts. First, we present a new algorithm for Bayesian Stackelberg games, called HUNTER, to scale up the number of types. HUNTER combines the following five key features: i) efficient pruning via a best-first search of the leader's strategy space; ii) a novel linear program for computing tight upper bounds for this search; iii) using Bender's decomposition for solving the upper bound linear program efficiently; iv) efficient inheritance of Bender's cuts from parent to child; v) an efficient heuristic branching rule. Our experiments show that HUNTER provides orders of magnitude speedups over the best existing methods to handle discrete follower types. In the second part, we show HUNTER's efficiency for Bayesian Stackelberg games can be exploited to also handle the continuous uncertainty using sample average approximation. We experimentally show that our HUNTER-based approach also outperforms latest robust solution methods under continuously distributed uncertainty.