Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Decentralised coordination of low-power embedded devices using the max-sum algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Nonmyopic informative path planning in spatio-temporal models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The generalized distributive law
IEEE Transactions on Information Theory
When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Analyzing the impact of human bias on human-agent teams in resource allocation domains
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Sensor placement and coordination via distributed multi-agent cooperative control
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Benchmarking hybrid algorithms for distributed constraint optimisation games
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
Agent-mediated multi-step optimization for resource allocation in distributed sensor networks
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Effective Variants of the Max-Sum Algorithm for Radar Coordination and Scheduling
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
The Knowledge Engineering Review
A tool for sensor placement and system monitoring in assistive environments
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Near-optimal continuous patrolling with teams of mobile information gathering agents
Artificial Intelligence
Risk-neutral bounded max-sum for distributed constraint optimization
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Improved max-sum algorithm for DCOP with n-ary constraints
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Coordinating multi-agent reinforcement learning with limited communication
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Agent-based decentralised coordination for sensor networks using the max-sum algorithm
Autonomous Agents and Multi-Agent Systems
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In this paper, we introduce an on-line, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. The algorithm enables sensors to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations using a Gaussian process. It builds upon the max-sum message passing algorithm for decentralised coordination, for which we present two new generic pruning techniques that result in speed-up of up to 92% for 5 sensors. We empirically evaluate our algorithm against several on-line adaptive coordination mechanisms, and report a reduction in root mean squared error up to 50% compared to a greedy strategy.