Article

Agent-organized networks for dynamic team formation

Authors:

Matthew E. Gaston,

Marie desJardinsAuthors Info & Claims

AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems

Pages 230 - 237

https://doi.org/10.1145/1082473.1082508

Published: 25 July 2005 Publication History

Abstract

Many multi-agent systems consist of a complex network of autonomous yet interdependent agents. Examples of such networked multi-agent systems include supply chains and sensor networks. In these systems, agents have a select set of other agents with whom they interact based on environmental knowledge, cognitive capabilities, resource limitations, and communications constraints. Previous findings have demonstrated that the structure of the artificial social network governing the agent interactions is strongly correlated with organizational performance. As multi-agent systems are typically embedded in dynamic environments, we wish to develop distributed, on-line network adaptation mechanisms for discovering effective network structures. Therefore, within the context of dynamic team formation, we propose several strategies for agent-organized networks (AONs) and evaluate their effectiveness for increasing organizational performance.

References

[1]

S. Abdallah and V. Lesser. Organization-based cooperative coalition formation. In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Techonology (IAT), September 2004.

Digital Library

[2]

G. Abramson and M. Kuperman. Social games in a social network. Phys. Rev. E, 63(030901), 2001.

[3]

R. Albert and A. Barabási. Statistical mechanics of complex networks. Review of Modern Physics, 99(3):7314--7316, May 2002.

[4]

M. Anghel, Z. Toroczkai, K. Bassler, and G. Korniss. Competition-driven network dynamics: Emergence of a scale-free leadership structure and collective efficiency. Physical Review Letters, 92(5), 2004.

[5]

R. C. Arkin and J. Diaz. Line-of-sight constrained exploration for reactive multiagent robotic teams. In Proceedings of the Seventh International Workshop on Advanced Motion Control (AMC'02), 2002.

[6]

R. Arunachalam, N. Sadeh, J. Eriksson, N. Finne, and S. Janson. The supply chain management game for the Trading Agent Competition 2004. Technical Report CMU-CS-04-107, Carnegie Mellon University School of Computer Science, July 2004.

[7]

R. Axtell. Effects of interaction topology and activation regime in several multi-agent systems. In MABS, pages 33--48, 2000.

Digital Library

[8]

D. Culler, D. Estrin, and M. Srivastava. Overview of sensor networks. IEEE Computer, 37(8):41--19, 2004.

Digital Library

[9]

J. Dall and M. Christensen. Random geometric graphs. Phys. Rev. E., 66(016121), 2002.

[10]

J. Delgado. Emergence of social conventions in complex networks. Artificial Intelligence, 141:171--185, 2002.

Digital Library

[11]

H. Ebel, J. Davidsen, and S. Bornholdt. Dynamics of social networks. Complexity, 8(2):24--27, 2002.

Digital Library

[12]

M. Fox, M. Barbuceanu, and R. Teigen. Agent-oriented supply-chain management. International Journal of Flexible Manufacturing Systems, 12(2/3):165--188, 2000.

[13]

M. Gaston and M. desJardins. Team formation in complex networks. In Proceedings of the 1st NAACSOS Conference, June 2003.

[14]

M. Gaston, J. Simmons, and M. desJardins. Adapting network structures for efficient team formation. In Proceedings of AAMAS-04 Workshop on Learning and Evolution in Agent-based Systems, July 2004.

[15]

P. Holme, A. Trusina, B. J. Kim, and P. Minnhagen. Prisoners' dilemma in real-world acquaintance networks: Spikes and quasi-equilibria induced by the interplay between structure and dynamics. Phys. Rev. E, 68(030901), 2003.

[16]

B. Horling, B. Benyo, and V. Lesser. Using self diagnosis to adapt organizational structure. In 5th International Conference on Autonomous Agents, 2001.

Digital Library

[17]

L. Hunsberger and B. Grosz. A combinatorial auction for collaborative planning. In Proceedings of the Fourth International Conference on Multi-Agent Systems (ICMAS-2000), 2000.

Digital Library

[18]

B. Kim, A. Trusina, P. Holme, P. Minnhagen, J. Chung, and M. Choi. Dynamics instabilities induced by asymmetric influence: Prisoners' dilemma game in small-world networks. Phys. Rev. E, 66(021907), 2002.

[19]

S. Kraus, O. Shehory, and G. Taase. Coalition formation with uncertain heterogeneous information. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS '03), July 2003.

Digital Library

[20]

P. J. Modi, H. Jung, M. Tambe, W.-M. Shen, and S. Kulkarni. A dynamic distributed constraint satisfaction approach to resource allocation. In Proceedings of the Seventh International Conference on Principles and Practice of Constraint Programming, 2001.

Digital Library

[21]

R. Nair, M. Tambe, and S. Marsella. Team formation for reformation. In Proceedings of the AAAI Spring Symposium on Intelligent Distributed and Embedded Systems, 2002.

[22]

R. Nair, M. Tambe, and S. Marsella. Role allocation and reallocation in multiagent teams: Towards a practical analysis. In Proceedings of the Second International Joint Conference on Agents and Multiagent Systems (AAMAS), 2003.

Digital Library

[23]

M. Newman. The structure and function of complex networks. SIAM Review, 45:167--256, 2003.

Digital Library

[24]

D. Pynadath and M. Tambe. Multiagent teamwork: Analyzing the optimality and complexity of key theories and models. In Proceedings of the Internation Conference on Autonomous Agents and Multiagent Systems (AAMAS '02), 2002.

Digital Library

[25]

F. Schweitzer and B. Tilch. Self-assembling of networks in an agent-based model. Physical Review E, 66:1--9, 2002.

[26]

B. Skyrms and R. Pemantle. A dynamic model of social network formation. Proceedings of the National Academy of Sciences USA, 97:9340--9346, 2000.

[27]

A. Szolnoki and G. Szabo. Phase transitions for rock-scissors-paper game on different networks. To appear in Phys. Rev. E, 2004.

[28]

M. Tambe. Towards flexible teamwork. Journal of Artificial Intelligence Research, 7:83--124, 1997.

[29]

H. P. Thadakamalla, U. N. Raghaven, S. Kumera, and R. Albert. Survivability of multiagent-based supply networks: A topological perspective. IEEE Intelligent Systems, 19(5):24--31, 2004.

Digital Library

[30]

D. Watts and S. Strogatz. Collective dynamics of 'small-world' networks. Nature, 393:440--442, 1998.

[31]

P. Yolum and M. P. Singh. Dynamic communities in referral networks. Web Intelligence and Agent Systems (WIAS), 1(2):105--116, 2003.

Digital Library

[32]

P. Yolum and M. P. Singh. Emergent personalized communities in referral networks. In Proceedings of the IJCAI Workshop on Intelligent Techniques for Web Personalization (ITWP), August 2003.

[33]

B. Yu and M. P. Singh. Searching social networks. In Proceedings of the 2nd International Joint Conference on Autonomous Agents and MultiAgent Systems (AAMAS). ACM Press, July 2003.

Digital Library

[34]

B. Yu, M. Venkatraman, and M. P. Singh. An adaptive social network for information access: Architecture and experimental results. Applied Artificial Intelligence, 17(1):21--38, January 2003.

Cited By

Valluru SSrivastava BPaladi SYan SNatarajan SWooldridge MDy JNatarajan S(2024)Promoting research collaboration with open data driven team recommendation in response to call for proposalsProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i21.30318(22833-22841)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i21.30318
Valluru SWidener MSrivastava BGangopadhyay S(2024)ULTRA: Exploring Team Recommendations in Two Geographies Using Open Data in Response to Call for ProposalsProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632503(547-552)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632503
Fenoy ABistaffa FFarinelli A(2024)An Attention Model for the Formation of Collectives in Real-World DomainsArtificial Intelligence10.1016/j.artint.2023.104064(104064)Online publication date: Jan-2024
https://doi.org/10.1016/j.artint.2023.104064
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Index Terms

Agent-organized networks for dynamic team formation
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

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cover image ACM Conferences

AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems

July 2005

1407 pages

ISBN:1595930930

DOI:10.1145/1082473

Program Chairs:
Michal Pechoucek
Czech Republic
,
Donald Steiner
USA
,
Simon Thompson
UK

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

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Published: 25 July 2005

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AAMAS05: AAMAS '05 - Fourth International Joint Conference on Autonomous Agents and Multiagent Systems 2005

July 25 - 29, 2005

The Netherlands

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

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Cited By

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https://dl.acm.org/doi/10.1609/aaai.v38i21.30318
Valluru SWidener MSrivastava BGangopadhyay S(2024)ULTRA: Exploring Team Recommendations in Two Geographies Using Open Data in Response to Call for ProposalsProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632503(547-552)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632503
Fenoy ABistaffa FFarinelli A(2024)An Attention Model for the Formation of Collectives in Real-World DomainsArtificial Intelligence10.1016/j.artint.2023.104064(104064)Online publication date: Jan-2024
https://doi.org/10.1016/j.artint.2023.104064
Kerkmann ARothe J(2024)The complexity of verifying popularity and strict popularity in altruistic hedonic gamesAutonomous Agents and Multi-Agent Systems10.1007/s10458-024-09679-038:2Online publication date: 1-Oct-2024
https://doi.org/10.1007/s10458-024-09679-0
Valluru SWidener MSrivastava BNatarajan SGangopadhyay S(2024)AI‐assisted research collaboration with open data for fair and effective response to call for proposalsAI Magazine10.1002/aaai.12203Online publication date: 21-Oct-2024
https://doi.org/10.1002/aaai.12203
Srivastava BKoppel TPaladi SValluru SSharma RBond O(2022)ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls2022 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW58026.2022.00130(1002-1009)Online publication date: Nov-2022
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Yang G(2022)Learning to Adapt for Agile OrganisationsProceedings of 2022 10th China Conference on Command and Control10.1007/978-981-19-6052-9_26(278-289)Online publication date: 30-Aug-2022
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Hu YLiu WLan JZhang J(2021)Forming Multi-agents Collaborative Communities in Overlapping Communities2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE)10.1109/RCAE53607.2021.9638948(114-120)Online publication date: 4-Nov-2021
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Nath ANiyogi R(2021)Distributed Framework for Task Execution with Quantitative SkillsComputational Science and Its Applications – ICCSA 202110.1007/978-3-030-87007-2_29(413-426)Online publication date: 11-Sep-2021
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Hughes EAnthony TEccles TLeibo JBalduzzi DBachrach YEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum GamesProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398827(538-547)Online publication date: 5-May-2020
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