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A Concise Review on Multiagent Teams: Contributions and Research Opportunities

  • Ewa AndrejczukEmail author
  • Juan A. Rodriguez-Aguilar
  • Carles Sierra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10207)

Abstract

The composition and formation of effective teams is crucial for both companies, to assure their competitiveness, and for a broad range of emerging applications exploiting multiagent collaboration (e.g. human-agent teamwork, crowdsourcing). The aim of this article is to provide an integrative perspective on team composition, team formation and their relationship with team performance. Thus, we review and classify the contributions in the computer science literature dealing with these topics. Our purpose is twofold. First, we intend to identify the strengths and weaknesses of the contributions made so far. Second, we pursue to identify research gaps and opportunities. Given the volume of the existing literature, our review is not intended to be exhaustive. Instead, we focus on the most recent contributions that broke new ground to spur innovative research.

References

  1. 1.
    Agmon, N., Barrett, S., Stone, P.: Modeling uncertainty in leading ad hoc teams. In: Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2014Google Scholar
  2. 2.
    Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Online team formation in social networks. In: Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, Lyon, France, 16–20 April 2012, pp. 839–848 (2012)Google Scholar
  3. 3.
    Andrejczuk, E., Rodríguez-Aguilar, J.A., Sierra, C.: Optimising congenial teams. In: International Workshop on Optimisation in Multi-Agent Systems (OPTMAS), 10 May 2016 (2016)Google Scholar
  4. 4.
    Andrejczuk, E., Berger, R., Rodriguez-Aguilar, J.A., Sierra, C., Marín-Puchades, V.: The composition and formation of effective teams. computer science meets psychology. arXiv preprint arXiv:1610.08804 (2016)
  5. 5.
    Barrett, S., Stone, P., Kraus, S., Rosenfeld, A.: Teamwork with limited knowledge of teammates. In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 2013Google Scholar
  6. 6.
    Chalkiadakis, G., Boutilier, C.: Sequentially optimal repeated coalition formation under uncertainty. Auton. Agents Multi-Agent Syst. 24(3), 441–484 (2012)CrossRefGoogle Scholar
  7. 7.
    Chen, B., Chen, X., Timsina, A., Soh, L.: Considering agent and task openness in ad hoc team formation. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, Istanbul, Turkey, 4–8 May 2015, pp. 1861–1862 (2015)Google Scholar
  8. 8.
    Crawford, C., Rahaman, Z., Sen, S.: Evaluating the efficiency of robust team formation algorithms. In: International Workshop on Optimisation in Multi-Agent Systems (2016)Google Scholar
  9. 9.
    Farhangian, M., Purvis, M., Purvis, M., Savarimuthu, T.B.R.: Agent-based modeling of resource allocation in software projects based on personality and skill. In: Koch, F., Guttmann, C., Busquets, D. (eds.) Advances in Social Computing and Multiagent Systems. CCIS, vol. 541, pp. 130–146. Springer, Cham (2015). doi: 10.1007/978-3-319-24804-2_9 CrossRefGoogle Scholar
  10. 10.
    Farhangian, M., Purvis, M.K., Purvis, M., Savarimuthu, B.T.R.: Modeling the effects of personality on team formation in self-assembly teams. In: Chen, Q., Torroni, P., Villata, S., Hsu, J., Omicini, A. (eds.) PRIMA 2015. LNCS (LNAI), vol. 9387, pp. 538–546. Springer, Cham (2015). doi: 10.1007/978-3-319-25524-8_36 CrossRefGoogle Scholar
  11. 11.
    Guzzo, R.A., Dickson, M.W.: Teams in organizations: recent research on performance and effectiveness. Annu. Rev. Psychol. 47(1), 307–338 (1996)CrossRefGoogle Scholar
  12. 12.
    Hackman, J.R.: Groups that Work (and Those That Don’t): Creating Conditions for Effective Teamwork. Jossey-Bass, San Francisco (1990). Number 10–H123Google Scholar
  13. 13.
    Hanna, N., Richards, D.: Do birds of a feather work better together? The impact of virtual agent personality on a shared mental model with humans during collaboration. In: Proceedings of the 3rd International Workshop on Collaborative Online Organizations, COOS 2016, co-located with the 14th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2015, Istanbul, Turkey, 4 May 2015, pp. 28–37 (2015)Google Scholar
  14. 14.
    Jennings, N.R., Moreau, L., Nicholson, D., Ramchurn, S., Roberts, S., Rodden, T., Rogers, A.: Human-agent collectives. Commun. ACM 57(12), 80–88 (2014)CrossRefGoogle Scholar
  15. 15.
    Kargar, M., An, A., Zihayat, M.: Efficient bi-objective team formation in social networks. In: Flach, P.A., Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS, vol. 7524, pp. 483–498. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33486-3_31 CrossRefGoogle Scholar
  16. 16.
    Kozlowski, S.W.J., Bell, B.S.: Work groups and teams in organizations. In: Handbook of Psychology. Wiley Online Library (2003)Google Scholar
  17. 17.
    Laal, M., Salamati, P.: Lifelong learning; why do we need it? Procedia-Soc. Behav. Sci. 31, 399–403 (2012)CrossRefGoogle Scholar
  18. 18.
    Liemhetcharat, S., Veloso, M.: Team formation with learning agents that improve coordination. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2014, Richland, SC, pp. 1531–1532. International Foundation for Autonomous Agents and Multiagent Systems (2014)Google Scholar
  19. 19.
    Liemhetcharat, S., Veloso, M.M.: Modeling and learning synergy for team formation with heterogeneous agents. In: International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, Valencia, Spain, 4–8 June 2012 (3 Volumes), pp. 365–374 (2012)Google Scholar
  20. 20.
    Marcolino, L.S., Jiang, A.X., Tambe, A.: Multi-agent team formation: diversity beats strength? In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, 3–9 August 2013 (2013)Google Scholar
  21. 21.
    Soriano Marcolino, L., Xu, H., Gerber, D., Kolev, B., Price, S., Pantazis, E., Tambe, M.: Multi-agent team formation for design problems. In: Dignum, V., Noriega, P., Sensoy, M., Sichman, J.S.S. (eds.) COIN 2015. LNCS, vol. 9628, pp. 354–375. Springer, Cham (2016). doi: 10.1007/978-3-319-42691-4_20 CrossRefGoogle Scholar
  22. 22.
    Nagarajan, V., Marcolino, L.S., Tambe, M.: Every team deserves a second chance: identifying when things go wrong (student abstract version). In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, USA, 25–30 January 2015, pp. 4184–4185 (2015)Google Scholar
  23. 23.
    Okimoto, T., Schwind, N., Clement, M., Ribeiro, T., Inoue, K., Marquis, P.: How to form a task-oriented robust team. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, pp. 395–403. International Foundation for Autonomous Agents and Multiagent Systems (2015)Google Scholar
  24. 24.
    Peleteiro, A., Burguillo-Rial, J.C., Luck, M., Arcos, J.L., Rodríguez-Aguilar, J.A.: Using reputation and adaptive coalitions to support collaboration in competitive environments. Eng. Appl. Artif. Intell. 45, 325–338 (2015)CrossRefGoogle Scholar
  25. 25.
    Podsakoff, P.M., MacKenzie, S.B., Ahearne, M.: Moderating effects of goal acceptance on the relationship between group cohesiveness and productivity. J. Appl. Psychol. 82(6), 974 (1997)CrossRefGoogle Scholar
  26. 26.
    Quijano, S., Navarro, J., Yepes, M., Berger, R., Romeo, M.: Human system audit (HSA) for the analysis of human behaviour in organizations. Papeles del Psicólogo 29(1), 92–106 (2008)Google Scholar
  27. 27.
    Rangapuram, S.S., Bühler, T., Hein, M.: Towards realistic team formation in social networks based on densest subgraphs. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1077–1088. ACM (2013)Google Scholar
  28. 28.
    Rangapuram, S.S., Bühler, T., Hein, M.: Towards realistic team formation in social networks based on densest subgraphs. CoRR, abs/1505.06661 (2015)Google Scholar
  29. 29.
    Rochlin, I., Aumann, Y., Sarne, D., Golosman, L.: Efficiency and fairness in team search with self-interested agents. Auton. Agent. Multi-Agent Syst. 30(3), 526–552 (2016)CrossRefGoogle Scholar
  30. 30.
    Rokicki, M., Zerr, S., Siersdorfer, S.: Groupsourcing: team competition designs for crowdsourcing. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015, Florence, Italy, 18–22 May 2015, pp. 906–915 (2015)Google Scholar
  31. 31.
    Spradling, M., Goldsmith, J., Liu, X., Dadi, C., Li, Z.: Roles and teams hedonic game. In: Perny, P., Pirlot, M., Tsoukiàs, A. (eds.) ADT 2013. LNCS (LNAI), vol. 8176, pp. 351–362. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41575-3_27 CrossRefGoogle Scholar
  32. 32.
    Wilde, D.J.: Teamology: The Construction and Organization of Effective Teams. Springer, London (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ewa Andrejczuk
    • 1
    • 2
    Email author
  • Juan A. Rodriguez-Aguilar
    • 1
  • Carles Sierra
    • 1
  1. 1.Artificial Intelligence Research Institute (IIIA-CSIC)BarcelonaSpain
  2. 2.Change Management Tool S.L.BarcelonaSpain

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