Bounded Approximate Decentralised Coordination using the Max-Sum Algorithm
Bounded Approximate Decentralised Coordination using the Max-Sum Algorithm
In this paper we propose a novel algorithm that provides bounded approximate solutions for decentralised coordination problems. Our approach removes cycles in any general constraint network by eliminating dependencies between functions and variables which have the least impact on the solution quality. It uses the max-sum algorithm to optimally solve the resulting tree structured constraint network, providing a bounded approximation specific to the particular problem instance. We formally prove that our algorithm provides a bounded approximation of the original problem and we present an empirical evaluation in a synthetic scenario. This shows that the approximate solutions that our algorithm provides are typically within 95% of the optimum and the approximation ratio that our algorithm provides is typically 1.23.
46-59
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Rogers, Alex
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Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
13 July 2009
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Farinelli, Alessandro, Rogers, Alex and Jennings, Nick
(2009)
Bounded Approximate Decentralised Coordination using the Max-Sum Algorithm.
IJCAI-09 Workshop on Distributed Constraint Reasoning (DCR), Pasadena, California, United States.
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Conference or Workshop Item
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Abstract
In this paper we propose a novel algorithm that provides bounded approximate solutions for decentralised coordination problems. Our approach removes cycles in any general constraint network by eliminating dependencies between functions and variables which have the least impact on the solution quality. It uses the max-sum algorithm to optimally solve the resulting tree structured constraint network, providing a bounded approximation specific to the particular problem instance. We formally prove that our algorithm provides a bounded approximation of the original problem and we present an empirical evaluation in a synthetic scenario. This shows that the approximate solutions that our algorithm provides are typically within 95% of the optimum and the approximation ratio that our algorithm provides is typically 1.23.
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ijcai_bounded.pdf
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Published date: 13 July 2009
Additional Information:
Event Dates: 13th July 2009
Venue - Dates:
IJCAI-09 Workshop on Distributed Constraint Reasoning (DCR), Pasadena, California, United States, 2009-07-13
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 267417
URI: http://eprints.soton.ac.uk/id/eprint/267417
PURE UUID: cfdb10fe-90cf-46e1-b7bd-9343c974131e
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Date deposited: 29 May 2009 09:21
Last modified: 14 Mar 2024 08:49
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Contributors
Author:
Alessandro Farinelli
Author:
Alex Rogers
Author:
Nick Jennings
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