Finding good approximate vertex and edge partitions is NP-hard
Information Processing Letters
Introduction to Algorithms
Stochastic Network Interdiction
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Security in multiagent systems by policy randomization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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
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
A double oracle algorithm for zero-sum security games on graphs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Adversarial Geospatial Abduction Problems
ACM Transactions on Intelligent Systems and Technology (TIST)
A unified method for handling discrete and continuous uncertainty in Bayesian Stackelberg games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
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The static asset protection problem (SAP) in a road network is that of allocating resources to protect vertices, given any possible behavior by an adversary determined to attack those assets. The dynamic asset protection (DAP) problem is a version of SAP where the asset is following a fixed and widely known route (e.g., a parade route) and needs to be protected. We formalize what it means for a given allocation of resources to be "optimal" for protecting a desired set of assets, and show that randomly allocating resources to a single edge cut in the road network solves this problem. Unlike SAP, we show that DAP is not only an NP-complete problem, but that approximating DAP is also NP-hard. We provide the GreedyDAP heuristic algorithm to solve DAP and show experimentally that it works well in practice, using road network data for real cities.