research-article

Coalition structure generation with the graphics processing unit

Authors:

Krzysztof Pawłowski,

Sarvapali Ramchurn,

Tomasz P. Michalak,

Talal RahwanAuthors Info & Claims

AAMAS '14: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems

Pages 293 - 300

Published: 05 May 2014 Publication History

Abstract

Coalition Structure Generation---the problem of finding the optimal division of agents into coalitions---has received considerable attention in recent AI literature. The fastest exact algorithm to solve this problem is IDP-IP$^*$, which is a hybrid of two previous algorithms, namely IDP and IP. Given this, it is desirable to speed up IDP as this will, in turn, improve upon the state-of-the-art. In this paper, we present IDP$^G$---the first coalition structure generation algorithm based on the \textit{Graphics Processing Unit (GPU)}. This follows a promising, new algorithm design paradigm that can provide significant speedups. We show that IDP$^G$ is faster than IDP by two orders of magnitude.

References

[1]

J. Adams and T. Service. Constant factor approximation algorithms for coalition structure generation. Autonomous Agents and Multi-Agent Systems, 23(1), 2011.

Digital Library

[2]

M. Arora. The Architecture and Evolution of CPU-GPU Systems for General Purpose Computing. Research survey, University of California, San Diego, 2012.

[3]

H. Aziz and B. de Keijzer. Complexity of coalition structure generation. In AAMAS, 2011.

Digital Library

[4]

Y. Bachrach and J. S. Rosenschein. Coalitional skill games. In AAMAS, 2008.

Digital Library

[5]

E. Bitar, E. Baeyens, P. Khargonekar, K. Poolla, and P. Varaiya. Optimal sharing of quantity risk for a coalition of wind power producers facing nodal prices. In ACC, 2012.

[6]

A. Bleiweiss. Gpu accelerated pathfinding. In Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS, pages 65--74. Eurographics Association, 2008.

Digital Library

[7]

V. Boyer, D. El Baz, and M. Elkihel. Solving knapsack problems on gpu. OR, 39(1), 2012.

Digital Library

[8]

Z. Du, Z. Yin, and D. Bader. A tile-based parallel viterbi algorithm for biological sequence alignment on gpu with cuda. In IPDPS - IEEE, pages 1--8. IEEE, 2010.

[9]

R. Glinton, P. Scerri, and K. Sycara. Agent-based sensor coalition formation. In Information Fusion, number Carnegie Mellon University-RI-TR-, 2008.

[10]

C. Li, K. Sycara, and A. Scheller-Wolf. Combinatorial coalition formation for multi-item group-buying with heterogeneous customers. Decision Support Systems, 49(1):1--13, 2010.

Digital Library

[11]

W. Lucas and R. Thrall. n-person games in partition function form. Naval Research Logistic Quarterly, pages 281--298, 1963.

[12]

F. C. Mencia. Optimizing performance for coalition structure generation problems in multicore systems. Master's thesis, Universitat Autonoma de Barcelona, Spain, 2012.

[13]

T. Michalak, J. Sroka, T. Rahwan, M. Wooldridge, P. McBurney, and N. Jennings. A distributed algorithm for anytime coalition structure generation. In AAMAS, 2010.

Digital Library

[14]

N. Ohta, V. Conitzer, R. Ichimura, Y. Sakurai, A. Iwasaki, and M. Yokoo. Coalition structure generation utilizing compact characteristic function representations. In CP, 2009.

Digital Library

[15]

J. Pan, C. Lauterbach, and D. Manocha. g-planner: Real-time motion planning and global navigation using gpus. In AAAI, pages 1245--1251, 2010.

[16]

T. Rahwan and N. R. Jennings. An improved dynamic programming algorithm for coalition structure generation. In AAMAS, 2008.

Digital Library

[17]

T. Rahwan, T. Michalak, and N. R. Jennings. A hybrid algorithm for coalition structure generation. In AAAI, 2012.

[18]

T. Rahwan, T. P. Michalak, E. Elkind, P. Faliszewski, J. Sroka, M. Wooldridge, and N. R. Jennings. Constrained coalition formation. In AAAI, 2011.

[19]

T. Rahwan, T. P. Michalak, and N. R. Jennings. Minimum search to establish worst-case guarantees in coalition structure generation. In IJCAI, 2011.

Digital Library

[20]

T. Rahwan, S. D. Ramchurn, A. Giovannucci, and N. R. Jennings. An anytime algorithm for optimal coalition structure generation. JAIR, 34:521--567, 2009.

[21]

S. Ryoo, C. I. Rodrigues, S. S. Baghsorkhi, S. S. Stone, D. B. Kirk, and W.-m. W. Hwu. Optimization principles and application performance evaluation of a multithreaded gpu using cuda. In ACM SIGPLAN, 2008.

Digital Library

[22]

T. W. Sandholm and V. R. Lesser. Coalitions among computationally bounded agents. Artifcial Intelligence, 94(1), 1997.

Digital Library

[23]

S. Tomov, J. Dongarra, and M. Baboulin. Towards dense linear algebra for hybrid gpu accelerated manycore systems. Parallel Computing, 36(5):232--240, 2010.

Digital Library

[24]

M. Tsvetovat, K. P. Sycara, Y. Chen, and J. Ying. Customer coalitions in the electronic marketplace. In AA, 2000.

Digital Library

[25]

S. Ueda, A. Iwasaki, M. Yokoo, M. C. Silaghi, K. Hirayama, and T. Matsui. Coalition structure generation based on distributed constraint optimization. In AAAI, 2010.

[26]

S. Ueda, M. Kitaki, A. Iwasaki, and M. Yokoo. Concise characteristic function representations in coalitional games based on agent types. In IJCAI, 2011.

Digital Library

[27]

D. Y. Yeh. A dynamic programming approach to the complete set partitioning problem. BIT Numerical Mathematics, 26(4):467--474, 1986.

Digital Library

[28]

K. Zhang and J. Kang. Real-time 4d signal processing and visualization using graphics processing unit on a regular nonlinear-k fourier-domain oct system. Optics express, 18(11):11772--11784, 2010.

Cited By

Bistaffa FFarinelli A(2018)A COP model for graph-constrained coalition formationJournal of Artificial Intelligence Research10.1613/jair.1.1120562:1(133-153)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1613/jair.1.11205
Riley LAtkinson KDunne PPayne TJonker CMarsella SThangarajah JTuyls K(2016)A Synergy Coalition Group based Dynamic Programming Algorithm for Coalition FormationProceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems10.5555/2936924.2937012(588-596)Online publication date: 9-May-2016
https://dl.acm.org/doi/10.5555/2936924.2937012

Index Terms

Coalition structure generation with the graphics processing unit
1. Computer systems organization

Recommendations

An anytime algorithm for optimal simultaneous coalition structure generation and assignment
Abstract
An important research problem in artificial intelligence is how to organize multiple agents, and coordinate them, so that they can work together to solve problems. Coordinating agents in a multi-agent system can significantly affect the system’s ...
Robust Coalition Structure Generation
PRIMA 2018: Principles and Practice of Multi-Agent Systems
Abstract
How to form effective coalitions is an important issue in multi-agent systems. Coalition Structure Generation () involves partitioning a set of agents into coalitions so that the social surplus (i.e. the sum of the rewards obtained by each ...
Online Coalition Structure Generation in Graph Games
AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

We consider the online version of the coalition structure generation in graph games problem, where agents are vertices in a graph. After each step t , in which the t -th agent appears in an online fashion, agents are partitioned into $c(t)$ coalitions $\...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

AAMAS '14: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems

May 2014

1774 pages

ISBN:9781450327381

General Chairs:
Ana Bazzan
UFRGS, Brazil
,
Michael Huhns
University of South Carolina, USA
,
Program Chairs:
Alessio Lomuscio
Imperial College London, UK
,
Paul Scerri
Carnegie Mellon University, USA

Sponsors

IFAAMAS

In-Cooperation

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 05 May 2014

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AAMAS '14

Sponsor:

AAMAS '14: International conference on Autonomous Agents and Multi-Agent Systems

May 5 - 9, 2014

Paris, France

Acceptance Rates

AAMAS '14 Paper Acceptance Rate 169 of 709 submissions, 24%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
66
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Bistaffa FFarinelli A(2018)A COP model for graph-constrained coalition formationJournal of Artificial Intelligence Research10.1613/jair.1.1120562:1(133-153)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1613/jair.1.11205
Riley LAtkinson KDunne PPayne TJonker CMarsella SThangarajah JTuyls K(2016)A Synergy Coalition Group based Dynamic Programming Algorithm for Coalition FormationProceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems10.5555/2936924.2937012(588-596)Online publication date: 9-May-2016
https://dl.acm.org/doi/10.5555/2936924.2937012

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents