SIAM Journal on Discrete Mathematics
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
ACM Computing Surveys (CSUR)
Multi-Agent Patrolling with Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
A Theoretical Analysis of Multi-Agent Patrolling Strategies
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multi-agent patrolling: an empirical analysis of alternative architectures
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
Negotiator agents for the patrolling task
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Autonomous multi-agent cycle based patrolling
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Multi-robot adversarial patrolling: facing a full-knowledge opponent
Journal of Artificial Intelligence Research
Heuristics for determining a patrol path of an unmanned combat vehicle
Computers and Industrial Engineering
Near-optimal continuous patrolling with teams of mobile information gathering agents
Artificial Intelligence
Multi-robot repeated area coverage
Autonomous Robots
Stochastic surveillance strategies for spatial quickest detection
International Journal of Robotics Research
Distributed multi-robot patrol: A scalable and fault-tolerant framework
Robotics and Autonomous Systems
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Patrolling refers to the act of walking around an area, with some regularity, in order to protect or supervise it. A group of agents is usually required to perform this task efficiently. Previous works in this field, using a metric that minimizes the period between visits to the same position, proposed static solutions that repeats a cycle over and over. But an efficient patrolling scheme requires unpredictability, so that the intruder cannot infer when the next visitation to a position will happen. This work presents various strategies to partition the sites among the agents, and to compute the visiting sequence. We evaluate these strategies using three metrics which approximates the probability of averting three types of intrusion - a random intruder, an intruder that waits until the guard leaves the site to initiate the attack, and an intruder that uses statistics to forecast how long the next visit to the site will be. We present the best strategies for each of these metrics, based on 500 simulations.