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Bounded Approximate Decentralised Coordination via the Max-Sum Algorithm

Bounded Approximate Decentralised Coordination via the Max-Sum Algorithm
Bounded Approximate Decentralised Coordination via the Max-Sum Algorithm
In this paper we propose a novel approach to decentralised coordination, that is able to efficiently compute solutions with a guaranteed approximation ratio. Our approach is based on a factor graph representation of the constraint network. It builds a tree structure by eliminating dependencies between the functions and variables within the factor graph that have the least impact on solution quality. It then uses the max-sum algorithm to optimally solve the resulting tree structured constraint network, and provides a bounded approximation specific to the particular problem instance. In addition, we present two generic pruning techniques to reduce the amount of computation that agents must perform when using the max-sum algorithm. When this is combined with the above mentioned approximation algorithm, the agents are able to solve decentralised coordination problems that have very large action spaces with a low computation and communication overhead. We empirically evaluate our approach in a mobile sensor domain, where mobile agents are used to monitor and predict the state of spatial phenomena (e.g., temperature or gas concentration). Such sensors need to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations. When applied in this domain, our approach is able to provide solutions which are guaranteed to be within 2% of the optimal solution. Moreover, the two pruning techniques are extremely effective in decreasing the computational effort of each agent by reducing the size of the search space by up to 92%.
max-sum, decentralised coordination, bounded approximation
730-759
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Stranders, Ruben
cca79d07-0668-4231-a80f-5fae6617644c
Jennings, Nicholas R
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Stranders, Ruben
cca79d07-0668-4231-a80f-5fae6617644c
Jennings, Nicholas R
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Rogers, Alex, Farinelli, Alessandro, Stranders, Ruben and Jennings, Nicholas R (2011) Bounded Approximate Decentralised Coordination via the Max-Sum Algorithm. Artificial Intelligence, 175 (2), 730-759. (doi:10.1016/j.artint.2010.11.001).

Record type: Article

Abstract

In this paper we propose a novel approach to decentralised coordination, that is able to efficiently compute solutions with a guaranteed approximation ratio. Our approach is based on a factor graph representation of the constraint network. It builds a tree structure by eliminating dependencies between the functions and variables within the factor graph that have the least impact on solution quality. It then uses the max-sum algorithm to optimally solve the resulting tree structured constraint network, and provides a bounded approximation specific to the particular problem instance. In addition, we present two generic pruning techniques to reduce the amount of computation that agents must perform when using the max-sum algorithm. When this is combined with the above mentioned approximation algorithm, the agents are able to solve decentralised coordination problems that have very large action spaces with a low computation and communication overhead. We empirically evaluate our approach in a mobile sensor domain, where mobile agents are used to monitor and predict the state of spatial phenomena (e.g., temperature or gas concentration). Such sensors need to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations. When applied in this domain, our approach is able to provide solutions which are guaranteed to be within 2% of the optimal solution. Moreover, the two pruning techniques are extremely effective in decreasing the computational effort of each agent by reducing the size of the search space by up to 92%.

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Published date: February 2011
Keywords: max-sum, decentralised coordination, bounded approximation
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 271664
URI: http://eprints.soton.ac.uk/id/eprint/271664
PURE UUID: bd396b48-f2c1-4547-986f-5e9c185e4d43

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Date deposited: 30 Oct 2010 11:00
Last modified: 14 Mar 2024 09:37

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Contributors

Author: Alex Rogers
Author: Alessandro Farinelli
Author: Ruben Stranders
Author: Nicholas R Jennings

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