Graph theory and its applications
Graph theory and its applications
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Algorithmic Game Theory
An efficient heuristic approach for security against multiple adversaries
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
RFID-based Communications for a Self-Organising Robot Swarm
SASO '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Probabilistic Multiagent Patrolling
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
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
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Nonmyopic informative path planning in spatio-temporal models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Decentralised coordination of mobile sensors using the max-sum algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 3 - Volume 3
Multi-robot area patrol under frequency constraints
Annals of Mathematics and Artificial Intelligence
Journal of Intelligent and Robotic Systems
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Symmetrizations for clustering directed graphs
Proceedings of the 14th International Conference on Extending Database Technology
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Lossy stochastic game abstraction with bounds
Proceedings of the 13th ACM Conference on Electronic Commerce
Computer Science Review
Dynamic programming meets the principle of inclusion and exclusion
Operations Research Letters
Distributed multi-robot patrol: A scalable and fault-tolerant framework
Robotics and Autonomous Systems
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Autonomous unmanned vehicles equipped with sensors are rapidly becoming the de facto means of achieving situational awareness - the ability to make sense of, and predict what is happening in an environment. Particularly in environments that are subject to continuous change, the use of such teams to maintain accurate and up-to-date situational awareness is a challenging problem. To perform well, the vehicles need to patrol their environment continuously and in a coordinated manner. To address this challenge, we develop a near-optimal multi-agent algorithm for continuously patrolling such environments. We first define a general class of multi-agent information gathering problems in which vehicles are represented by information gathering agents - autonomous entities that direct their activity towards collecting information with the aim of providing accurate and up-to-date situational awareness. These agents move on a graph, while taking measurements with the aim of maximising the cumulative discounted observation value over time. Here, observation value is an abstract measure of reward, which encodes the properties of the agents@? sensors, and the spatial and temporal properties of the measured phenomena. Concrete instantiations of this class of problems include monitoring environmental phenomena (temperature, pressure, etc.), disaster response, and patrolling environments to prevent intrusions from (non-strategic) attackers. In more detail, we derive a single-agent divide and conquer algorithm to compute a continuous patrol (an infinitely long path in the graph) that yields a near-optimal amount of observation value. This algorithm recursively decomposes the graph, until high-quality paths in the resulting components can be computed outright by a greedy algorithm. It then constructs a patrol by concatenating these paths using dynamic programming. For multiple agents, the algorithm sequentially computes patrols for each agent in a greedy fashion, in order to maximise its marginal contribution to the team. Moreover, to achieve robustness, we develop algorithms for repairing patrols when one or more agents fail or the graph changes. For both the single- and the multi-agent case, we give theoretical guarantees (lower bounds on the solution quality and an upper bound on the computational complexity in the size of the graph and the number agents) on the performance of the algorithms. We benchmark the single- and multi-agent algorithm against the state of the art and demonstrate that it typically performs 35% and 33% better in terms of average and minimum solution quality respectively.