Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Stackelberg scheduling strategies
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Introduction to Linear Optimization
Introduction to Linear Optimization
Playing large games using simple strategies
Proceedings of the 4th ACM conference on Electronic commerce
Computing approximate bayes-nash equilibria in tree-games of incomplete information
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Theoretical Analysis of the Multi-agent Patrolling Problem
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Computing the optimal strategy to commit to
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Virtual patrol: a new power conservation design for surveillance using sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Security in multiagent systems by policy randomization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Mixed-integer programming methods for finding Nash equilibria
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Generating and solving imperfect information games
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Multi-agent patrolling: an empirical analysis of alternative architectures
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
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
A realistic model of frequency-based multi-robot polyline patrolling
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
Coordinating randomized policies for increasing security of agent systems
Information Technology and Management
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
Uncertainties in adversarial patrol
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Game Theoretical Insights in Strategic Patrolling: Model and Algorithm in Normal-Form
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Efficient algorithms to solve Bayesian Stackelberg games for security applications
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Multi-a(ge)nt Graph Patrolling and Partitioning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Adversary aware surveillance systems
IEEE Transactions on Information Forensics and Security
Adversarial uncertainty in multi-robot patrol
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Capturing augmented sensing capabilities and intrusion delay in patrolling-intrusion games
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
On events in multi-robot patrol in adversarial environments
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 2 - Volume 2
Multi-robot area patrol under frequency constraints
Annals of Mathematics and Artificial Intelligence
Operations Research
Multi-robot adversarial patrolling: facing a full-knowledge opponent
Journal of Artificial Intelligence Research
Playing repeated Stackelberg games with unknown opponents
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
Near-optimal continuous patrolling with teams of mobile information gathering agents
Artificial Intelligence
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In adversarial multiagent domains, security, commonly defined as the ability to deal with intentional threats from other agents, is a critical issue. This paper focuses on domains where these threats come from unknown adversaries. These domains can be modeled as Bayesian games; much work has been done on finding equilibria for such games. However, it is often the case in multiagent security domains that one agent can commit to a mixed strategy which its adversaries observe before choosing their own strategies. In this case, the agent can maximize reward by finding an optimal strategy, without requiring equilibrium. Previous work has shown this problem of optimal strategy selection to be NP-hard. Therefore, we present a heuristic called ASAP, with three key advantages to address the problem. First, ASAP searches for the highest-reward strategy, rather than a Bayes-Nash equilibrium, allowing it to find feasible strategies that exploit the natural first-mover advantage of the game. Second, it provides strategies which are simple to understand, represent, and implement. Third, it operates directly on the compact, Bayesian game representation, without requiring conversion to normal form. We provide an efficient Mixed Integer Linear Program (MILP) implementation for ASAP, along with experimental results illustrating significant speedups and higher rewards over other approaches.