RoboCup Rescue: A Grand Challenge for Multi-Agent Systems
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Ad hoc Networking
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
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
EURASIP Journal on Wireless Communications and Networking - Cognitive Radio and Dynamic Spectrum Sharing Systems
Reward shaping for valuing communications during multi-agent coordination
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Congestion games with resource reuse and applications in spectrum sharing
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
A Cooperative Multiagent Based Spectrum Sharing
AICT '10 Proceedings of the 2010 Sixth Advanced International Conference on Telecommunications
The complexity of decentralized control of Markov decision processes
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Learning to cooperate via policy search
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Power line communications: state of the art and future trends
IEEE Communications Magazine
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In a wide range of emerging applications, from disaster management to intelligent sensor networks, teams of software agents can be deployed to effectively solve complex distributed problems. To achieve this, agents typically need to communicate locally sensed information to each other. However, in many settings, there are heavy constraints on the communication infrastructure, making it infeasible for every agent to broadcast all relevant information to everyone else. To address this challenge, we investigate how agents can make good local decisions about what information to send to a set of communication channels with limited bandwidths such that the overall system utility is maximised. Specifically, to solve this problem efficiently in large-scale systems with hundreds or thousands of agents, we develop a novel decentralised algorithm. This combines multi-agent learning techniques with fast decision-theoretic reasoning mechanisms that predict the impact a single agent has on the entire system. We show empirically that our algorithm consistently achieves 85% of a hypothetical centralised optimal strategy with full information, and that it significantly outperforms a number of baseline benchmarks (by up to 600%).