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An efficient vector-based representation for coalitional games

An efficient vector-based representation for coalitional games
An efficient vector-based representation for coalitional games
We propose a new representation for coalitional games, called the coalitional skill vector model, where there is a set of skills in the system, and each agent has a skill vector--a vector consisting of values that reflect the agents' level in different skills. Furthermore, there is a set of goals, each with requirements expressed in terms of the minimum skill level necessary to achieve the goal. Agents can form coalitions to aggregate their skills, and achieve goals otherwise unachievable. We show that this representation is fully expressive, that is, it can represent any characteristic function game. We also show that, for some interesting classes of games, our representation is significantly more compact than the classical representation, and facilitates the development of efficient algorithms to solve the coalition structure generation problem, as well as the problem of computing the core and/or the least core. We also demonstrate that by using the coalitional skill vector representation, our solver can handle up to 500 agents.
978-1-57735-633-2
383-389
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Nguyen, Tri-Dung
a6aa7081-6bf7-488a-b72f-510328958a8e
Rahwan, Talal
476029f3-5484-4747-9f44-f63f3687083c
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Nguyen, Tri-Dung
a6aa7081-6bf7-488a-b72f-510328958a8e
Rahwan, Talal
476029f3-5484-4747-9f44-f63f3687083c
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Tran-Thanh, Long, Nguyen, Tri-Dung, Rahwan, Talal, Rogers, Alex and Jennings, N. R. (2013) An efficient vector-based representation for coalitional games. IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence. pp. 383-389 .

Record type: Conference or Workshop Item (Paper)

Abstract

We propose a new representation for coalitional games, called the coalitional skill vector model, where there is a set of skills in the system, and each agent has a skill vector--a vector consisting of values that reflect the agents' level in different skills. Furthermore, there is a set of goals, each with requirements expressed in terms of the minimum skill level necessary to achieve the goal. Agents can form coalitions to aggregate their skills, and achieve goals otherwise unachievable. We show that this representation is fully expressive, that is, it can represent any characteristic function game. We also show that, for some interesting classes of games, our representation is significantly more compact than the classical representation, and facilitates the development of efficient algorithms to solve the coalition structure generation problem, as well as the problem of computing the core and/or the least core. We also demonstrate that by using the coalitional skill vector representation, our solver can handle up to 500 agents.

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More information

Published date: 2013
Venue - Dates: IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013-01-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 351024
URI: https://eprints.soton.ac.uk/id/eprint/351024
ISBN: 978-1-57735-633-2
PURE UUID: 2eae5186-2c98-4e18-85ca-3a489a20e922
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316

Catalogue record

Date deposited: 12 Apr 2013 15:50
Last modified: 06 Jun 2018 12:35

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