Standard
The power of teams that disagree: team formation in large action spaces. /
Soriano Marcolino, Leandro; Xu, Haifeng; Xin Jiang, Albert et al.
Coordination, Organizations, Institutions and Norms in Agent Systems X: COIN 2014 International Workshops, COIN@AAMAS, Paris, France, May 6, 2014, COIN@PRICAI, Gold Coast, QLD, Australia, December 4, 2014, Revised Selected Papers. ed. / Aditya Ghose; Nir Oren; Pankaj Telang; John Thangarajah. Springer, 2015. p. 213-232 (Lecture Notes in Computer Science; Vol. 9372).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review
Harvard
Soriano Marcolino, L, Xu, H, Xin Jiang, A, Tambe, M & Bowring, E 2015,
The power of teams that disagree: team formation in large action spaces. in A Ghose, N Oren, P Telang & J Thangarajah (eds),
Coordination, Organizations, Institutions and Norms in Agent Systems X: COIN 2014 International Workshops, COIN@AAMAS, Paris, France, May 6, 2014, COIN@PRICAI, Gold Coast, QLD, Australia, December 4, 2014, Revised Selected Papers. Lecture Notes in Computer Science, vol. 9372, Springer, pp. 213-232. <
http://link.springer.com/chapter/10.1007%2F978-3-319-25420-3_14>
APA
Soriano Marcolino, L., Xu, H., Xin Jiang, A., Tambe, M., & Bowring, E. (2015).
The power of teams that disagree: team formation in large action spaces. In A. Ghose, N. Oren, P. Telang, & J. Thangarajah (Eds.),
Coordination, Organizations, Institutions and Norms in Agent Systems X: COIN 2014 International Workshops, COIN@AAMAS, Paris, France, May 6, 2014, COIN@PRICAI, Gold Coast, QLD, Australia, December 4, 2014, Revised Selected Papers (pp. 213-232). (Lecture Notes in Computer Science; Vol. 9372). Springer.
http://link.springer.com/chapter/10.1007%2F978-3-319-25420-3_14
Vancouver
Soriano Marcolino L, Xu H, Xin Jiang A, Tambe M, Bowring E.
The power of teams that disagree: team formation in large action spaces. In Ghose A, Oren N, Telang P, Thangarajah J, editors, Coordination, Organizations, Institutions and Norms in Agent Systems X: COIN 2014 International Workshops, COIN@AAMAS, Paris, France, May 6, 2014, COIN@PRICAI, Gold Coast, QLD, Australia, December 4, 2014, Revised Selected Papers. Springer. 2015. p. 213-232. (Lecture Notes in Computer Science).
Author
Soriano Marcolino, Leandro ; Xu, Haifeng ; Xin Jiang, Albert et al. /
The power of teams that disagree : team formation in large action spaces. Coordination, Organizations, Institutions and Norms in Agent Systems X: COIN 2014 International Workshops, COIN@AAMAS, Paris, France, May 6, 2014, COIN@PRICAI, Gold Coast, QLD, Australia, December 4, 2014, Revised Selected Papers. editor / Aditya Ghose ; Nir Oren ; Pankaj Telang ; John Thangarajah. Springer, 2015. pp. 213-232 (Lecture Notes in Computer Science).
Bibtex
@inbook{7fd066be7489458393562d899ec3965f,
title = "The power of teams that disagree: team formation in large action spaces",
abstract = "Recent work has shown that diverse teams can outperform a uniform team made of copies of the best agent. However, there are fundamental questions that were never asked before. When should we use diverse or uniform teams? How does the performance change as the action space or the teams get larger? Hence, we present a new model of diversity, where we prove that the performance of a diverse team improves as the size of the action space increases. Moreover, we show that the performance converges exponentially fast to the optimal one as we increase the number of agents. We present synthetic experiments that give further insights: even though a diverse team outperforms a uniform team when the size of the action space increases, the uniform team will eventually again play better than the diverse team for a large enough action space. We verify our predictions in a system of Go playing agents, where a diverse team improves in performance as the board size increases, and eventually overcomes a uniform team.",
keywords = "Coordination & Collaboration, Distributed AI , Team Formation",
author = "{Soriano Marcolino}, Leandro and Haifeng Xu and {Xin Jiang}, Albert and Milind Tambe and Emma Bowring",
year = "2015",
month = nov,
day = "7",
language = "English",
isbn = "9783319254197",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "213--232",
editor = "Aditya Ghose and Nir Oren and Pankaj Telang and John Thangarajah",
booktitle = "Coordination, Organizations, Institutions and Norms in Agent Systems X",
}
RIS
TY - CHAP
T1 - The power of teams that disagree
T2 - team formation in large action spaces
AU - Soriano Marcolino, Leandro
AU - Xu, Haifeng
AU - Xin Jiang, Albert
AU - Tambe, Milind
AU - Bowring, Emma
PY - 2015/11/7
Y1 - 2015/11/7
N2 - Recent work has shown that diverse teams can outperform a uniform team made of copies of the best agent. However, there are fundamental questions that were never asked before. When should we use diverse or uniform teams? How does the performance change as the action space or the teams get larger? Hence, we present a new model of diversity, where we prove that the performance of a diverse team improves as the size of the action space increases. Moreover, we show that the performance converges exponentially fast to the optimal one as we increase the number of agents. We present synthetic experiments that give further insights: even though a diverse team outperforms a uniform team when the size of the action space increases, the uniform team will eventually again play better than the diverse team for a large enough action space. We verify our predictions in a system of Go playing agents, where a diverse team improves in performance as the board size increases, and eventually overcomes a uniform team.
AB - Recent work has shown that diverse teams can outperform a uniform team made of copies of the best agent. However, there are fundamental questions that were never asked before. When should we use diverse or uniform teams? How does the performance change as the action space or the teams get larger? Hence, we present a new model of diversity, where we prove that the performance of a diverse team improves as the size of the action space increases. Moreover, we show that the performance converges exponentially fast to the optimal one as we increase the number of agents. We present synthetic experiments that give further insights: even though a diverse team outperforms a uniform team when the size of the action space increases, the uniform team will eventually again play better than the diverse team for a large enough action space. We verify our predictions in a system of Go playing agents, where a diverse team improves in performance as the board size increases, and eventually overcomes a uniform team.
KW - Coordination & Collaboration
KW - Distributed AI
KW - Team Formation
M3 - Chapter (peer-reviewed)
SN - 9783319254197
T3 - Lecture Notes in Computer Science
SP - 213
EP - 232
BT - Coordination, Organizations, Institutions and Norms in Agent Systems X
A2 - Ghose, Aditya
A2 - Oren, Nir
A2 - Telang, Pankaj
A2 - Thangarajah, John
PB - Springer
ER -
