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A hierarchical clustering approach to large-scale near-optimal coalition formation with quality guarantees

A hierarchical clustering approach to large-scale near-optimal coalition formation with quality guarantees
A hierarchical clustering approach to large-scale near-optimal coalition formation with quality guarantees
Coalition formation is a fundamental approach to multi-agent coordination, and a key challenge in this context is the coalition structure generation problem, where a set of agents must be partitioned into the best set of coalitions. This problem is NP-hard and typical optimal algorithms do not scale to more than 50 agents: efficient approximate solutions are therefore needed for hundreds or thousands of agents. In this paper we propose a novel heuristic, based on ideas and tools used in the data clustering domain. In particular, we present a coalition formation algorithm inspired by the well known class of hierarchical agglomerative clustering techniques (Linkage algorithms). We present different variants of the algorithm, which we call Coalition Linkage (C-Link) and demonstrate how such algorithm can be adapted to graph restricted coalition formation problems (where an interaction graph defined among the agents restricts the set of feasible coalitions). Moreover, we discuss how we can provide an upper bound on the value of the optimal coalition structure, and we show that for specific characteristic functions we can provide such bounds while maintaining polynomial computational costs and memory requirements. We empirically evaluate the different variants of the CLink algorithm on two synthetic benchmark data-sets, as well as in two real world scenarios, involving a collective energy purchasing and a ride-sharing application. In these settings C-Link achieves promising results providing high quality solutions and solving problem involving thousands of agents in few minutes.
0952-1976
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Bicego, Manuele
5ce9ea10-73a1-47a1-bcce-9cc45ba588fa
Bistaffa, Filippo
c3867bb6-ac44-472e-bb89-e5ed315cdedd
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Farinelli, Alessandro
d2f26070-f403-4cae-b712-7097cb2e3fc6
Bicego, Manuele
5ce9ea10-73a1-47a1-bcce-9cc45ba588fa
Bistaffa, Filippo
c3867bb6-ac44-472e-bb89-e5ed315cdedd
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3

Farinelli, Alessandro, Bicego, Manuele, Bistaffa, Filippo and Ramchurn, Sarvapali D. (2016) A hierarchical clustering approach to large-scale near-optimal coalition formation with quality guarantees. Engineering Applications of Artificial Intelligence. (In Press)

Record type: Article

Abstract

Coalition formation is a fundamental approach to multi-agent coordination, and a key challenge in this context is the coalition structure generation problem, where a set of agents must be partitioned into the best set of coalitions. This problem is NP-hard and typical optimal algorithms do not scale to more than 50 agents: efficient approximate solutions are therefore needed for hundreds or thousands of agents. In this paper we propose a novel heuristic, based on ideas and tools used in the data clustering domain. In particular, we present a coalition formation algorithm inspired by the well known class of hierarchical agglomerative clustering techniques (Linkage algorithms). We present different variants of the algorithm, which we call Coalition Linkage (C-Link) and demonstrate how such algorithm can be adapted to graph restricted coalition formation problems (where an interaction graph defined among the agents restricts the set of feasible coalitions). Moreover, we discuss how we can provide an upper bound on the value of the optimal coalition structure, and we show that for specific characteristic functions we can provide such bounds while maintaining polynomial computational costs and memory requirements. We empirically evaluate the different variants of the CLink algorithm on two synthetic benchmark data-sets, as well as in two real world scenarios, involving a collective energy purchasing and a ride-sharing application. In these settings C-Link achieves promising results providing high quality solutions and solving problem involving thousands of agents in few minutes.

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Accepted/In Press date: 20 December 2016

Identifiers

Local EPrints ID: 406719
URI: http://eprints.soton.ac.uk/id/eprint/406719
ISSN: 0952-1976
PURE UUID: 7e35517d-8898-4187-a181-02365006a30d
ORCID for Sarvapali D. Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

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Date deposited: 21 Mar 2017 02:02
Last modified: 29 Apr 2020 04:02

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Contributors

Author: Alessandro Farinelli
Author: Manuele Bicego
Author: Filippo Bistaffa
Author: Sarvapali D. Ramchurn ORCID iD

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