An efficient vector-based representation for coalitional games

Long Tran-Thanh, Tri Dung Nguyen, Talal Rahwan, Alex Rogers, Nicholas R. Jennings

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages383-389
Number of pages7
StatePublished - Dec 1 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period8/3/138/9/13

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Tran-Thanh, L., Nguyen, T. D., Rahwan, T., Rogers, A., & Jennings, N. R. (2013). An efficient vector-based representation for coalitional games. In IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence (pp. 383-389)

An efficient vector-based representation for coalitional games. / Tran-Thanh, Long; Nguyen, Tri Dung; Rahwan, Talal; Rogers, Alex; Jennings, Nicholas R.

IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013. p. 383-389.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tran-Thanh, L, Nguyen, TD, Rahwan, T, Rogers, A & Jennings, NR 2013, An efficient vector-based representation for coalitional games. in IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence. pp. 383-389, 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, 8/3/13.
Tran-Thanh L, Nguyen TD, Rahwan T, Rogers A, Jennings NR. An efficient vector-based representation for coalitional games. In IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013. p. 383-389
Tran-Thanh, Long ; Nguyen, Tri Dung ; Rahwan, Talal ; Rogers, Alex ; Jennings, Nicholas R. / An efficient vector-based representation for coalitional games. IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013. pp. 383-389
@inproceedings{4dff7f4c86244fe4adc6df2f8886541f,
title = "An efficient vector-based representation for coalitional games",
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.",
author = "Long Tran-Thanh and Nguyen, {Tri Dung} and Talal Rahwan and Alex Rogers and Jennings, {Nicholas R.}",
year = "2013",
month = "12",
day = "1",
language = "English (US)",
isbn = "9781577356332",
pages = "383--389",
booktitle = "IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence",

}

TY - GEN

T1 - An efficient vector-based representation for coalitional games

AU - Tran-Thanh, Long

AU - Nguyen, Tri Dung

AU - Rahwan, Talal

AU - Rogers, Alex

AU - Jennings, Nicholas R.

PY - 2013/12/1

Y1 - 2013/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84896061523&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84896061523&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781577356332

SP - 383

EP - 389

BT - IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence

ER -