Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Constraint Processing
RoboCup Rescue: A Grand Challenge for Multi-Agent Systems
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Distributed Sensor Networks: A Multiagent Perspective
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Information Theory, Inference & Learning Algorithms
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Network coverage using low duty-cycled sensors: random & coordinated sleep algorithms
Proceedings of the 3rd international symposium on Information processing in sensor networks
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
On k-coverage in a mostly sleeping sensor network
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Power management in energy harvesting sensor networks
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ACM Transactions on Sensor Networks (TOSN)
Decentralised coordination of continuously valued control parameters using the max-sum algorithm
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Decentralised coordination of mobile sensors using the max-sum algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Dynamic configuration of agent organizations
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Decentralized scattering of wake-up times in wireless sensor networks
EWSN'07 Proceedings of the 4th European conference on Wireless sensor networks
Decentralized Coordination in RoboCup Rescue
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Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Autonomous Agents and Multi-Agent Systems
Resource-aware junction trees for efficient multi-agent coordination
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Distributed model shaping for scaling to decentralized POMDPs with hundreds of agents
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Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The complexity of decentralized control of Markov decision processes
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Self-organized routing for wireless microsensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Decentralized Bayesian reinforcement learning for online agent collaboration
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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In this paper, we consider the generic problem of how a network of physically distributed, computationally constrained devices can make coordinated decisions to maximise the effectiveness of the whole sensor network. In particular, we propose a new agent-based representation of the problem, based on the factor graph, and use state-of-the-art DCOP heuristics (i.e., DSA and the max-sum algorithm) to generate sub-optimal solutions. In more detail, we formally model a specific real-world problem where energy-harvesting sensors are deployed within an urban environment to detect vehicle movements. The sensors coordinate their sense/sleep schedules, maintaining energy neutral operation while maximising vehicle detection probability. We theoretically analyse the performance of the sensor network for various coordination strategies and show that by appropriately coordinating their schedules the sensors can achieve significantly improved system-wide performance, detecting up to 50 % of the events that a randomly coordinated network fails to detect. Finally, we deploy our coordination approach in a realistic simulation of our wide area surveillance problem, comparing its performance to a number of benchmarking coordination strategies. In this setting, our approach achieves up to a 57 % reduction in the number of missed vehicles (compared to an uncoordinated network). This performance is close to that achieved by a benchmark centralised algorithm (simulated annealing) and to a continuously powered network (which is an unreachable upper bound for any coordination approach).