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Decentralised channel allocation and information sharing for teams of cooperative agents

Decentralised channel allocation and information sharing for teams of cooperative agents
Decentralised channel allocation and information sharing for teams of cooperative agents
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%).
231-238
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Williamson, Simon
28eaa4d9-5fcb-410e-91b6-0b42d4513a75
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Williamson, Simon
28eaa4d9-5fcb-410e-91b6-0b42d4513a75
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Stein, Sebastian, Williamson, Simon and Jennings, Nick (2012) Decentralised channel allocation and information sharing for teams of cooperative agents. Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Spain. pp. 231-238 .

Record type: Conference or Workshop Item (Paper)

Abstract

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%).

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More information

Published date: 2012
Venue - Dates: Proc. 11th Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Spain, 2012-01-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 273085
URI: https://eprints.soton.ac.uk/id/eprint/273085
PURE UUID: e4cbd9a3-43bd-473b-8e57-a08057c052f1

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Date deposited: 02 Jan 2012 10:59
Last modified: 18 Jul 2017 06:17

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