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C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation

C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation
C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation
Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes.
978-1-57735-633-2
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Bicego, Manuele
5ce9ea10-73a1-47a1-bcce-9cc45ba588fa
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Zuchelli, Marco
48511230-2d6d-4dae-b037-57d9dd5d6a3d
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Bicego, Manuele
5ce9ea10-73a1-47a1-bcce-9cc45ba588fa
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Zuchelli, Marco
48511230-2d6d-4dae-b037-57d9dd5d6a3d

Farinelli, Alessandro, Bicego, Manuele, Ramchurn, Sarvapali and Zuchelli, Marco (2013) C-Link: a hierarchical clustering approach to large-scale near-optimal coalition formation. 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), China. 03 - 09 Aug 2013. 7 pp.

Record type: Conference or Workshop Item (Paper)

Abstract

Coalition formation is a fundamental approach to multi-agent coordination. In this paper we address the specific problem of coalition structure generation, and focus on providing good-enough solutions using a novel heuristic approach that is based on data clustering methods. In particular, we propose a hierarchical agglomerative clustering approach (C-Link), which uses a similarity criterion between coalitions based on the gain that the system achieves if two coalitions merge. We empirically evaluate C-Link on a synthetic benchmark data-set as well as in collective energy purchasing settings. Our results show that the C-link approach performs very well against an optimal benchmark based on Mixed-Integer Programming, achieving solutions which are in the worst case about 80% of the optimal (in the synthetic data-set), and 98% of the optimal (in the energy data-set). Thus we show that C-Link can return solutions for problems involving thousands of agents within minutes.

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More information

e-pub ahead of print date: August 2013
Venue - Dates: 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), China, 2013-08-03 - 2013-08-09
Related URLs:
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 351521
URI: https://eprints.soton.ac.uk/id/eprint/351521
ISBN: 978-1-57735-633-2
PURE UUID: f09c16bf-1636-477e-8d18-4a1477262d03
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

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Date deposited: 26 Apr 2013 14:34
Last modified: 06 Jun 2018 12:42

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