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
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi-step multi-sensor hider-seeker games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Weighted random sampling with a reservoir
Information Processing Letters
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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
A double oracle algorithm for zero-sum security games on graphs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multiagent Communication Security in Adversarial Settings
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Security scheduling for real-world networks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Hi-index | 0.00 |
Moving assets through a transportation network is a crucial challenge in hostile environments such as future battlefields where malicious adversaries have strong incentives to attack vulnerable patrols and supply convoys. Intelligent agents must balance network costs with the harm that can be inflicted by adversaries who are in turn acting rationally to maximize harm while trading off against their own costs to attack. Furthermore, agents must choose their strategies even without full knowledge of their adversaries' capabilities, costs, or incentives. In this paper we model this problem as a non-zero sum game between two players, a sender who chooses flows through the network and an adversary who chooses attacks on the network. We advance the state of the art by: (1) moving beyond the zero-sum games previously considered to non-zero sum games where the adversary incurs attack costs that are not incorporated into the payoff of the sender; (2) introducing a refinement of the Stackelberg equilibrium that is more appropriate to network security games than previous solution concepts; and (3) using Bayesian games where the sender is uncertain of the capabilities, payoffs, and costs of the adversary. We provide polynomial time algorithms for finding equilibria in each of these cases. We also show how our approach can be applied to games where there are multiple adversaries.