Skip to main content
SpringerLink
Log in

Allocating Teams to Tasks: An Anytime Heuristic Competence-Based Approach

  • Conference paper
  • First Online:
Multi-Agent Systems (EUMAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13442))

Included in the following conference series:

Abstract

Many practical applications often need to form a team of agents to solve a task since no agent alone has the full set of required competencies to complete the task on time. Here we address the problem of distributing individuals in non-overlapping teams, each team in charge of a specific task. We provide the formalisation of the problem, we encode it as a linear program and show how hard it is to solve it. Given this, we propose an anytime heuristic algorithm that yields feasible team allocations that are good enough solutions. Finally, we report the results of an experimental evaluation over the concrete problem of matching teams of students to internship programs in companies.

*Research supported by projects AI4EU (H2020-825619), TAILOR (H2020-952215), 2019DI17, Humane-AI-Net (H2020-952026), Crowd4SDG (H2020-872944), and grant PID2019-104156GB-I00 funded by MCIN/AEI/10.13039/501100011033.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This work is an extended version of our earlier work presented in [16, 17].

  2. 2.

    Note: we use subscript a to refer to the set of competences and the identifier of an agent \(a\in A\), and subscript \(\tau \) to refer to the elements of task \(\tau \in T\).

  3. 3.

    As noted by [22], recent definitions on the term team refer to the specific subtask/ competences that will be performed by each agent.

  4. 4.

    As noted in [9], the product favours both increases in overall team utility and inequality-reducing distributions of individuals’ contributing values.

  5. 5.

    Note that the NOMTMT allocation problem is interrelated with the |T| optimisation problems. However, for a fixed team allocation, the inner optimisation problems are independent from one another.

  6. 6.

    Notably, the marks applied by the evaluators indicate rankings and therefore these numbers are meaningless; thus we turn to tournaments.

References

  1. Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Power in unity: forming teams in large-scale community systems. In: Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, 26-30 October 2010, pp. 599–608, January 2010

    Google Scholar 

  2. Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Online team formation in social networks. In: Proceedings of the 21st International Conference on World Wide Web. pp. 839–848. WWW 2012, Association for Computing Machinery, New York, NY, USA (2012)

    Google Scholar 

  3. Andrejczuk, E., Berger, R., Rodríguez-Aguilar, J.A., Sierra, C., Marín-Puchades, V.: The composition and formation of effective teams: computer science meets organizational psychology. Knowledge Eng. Rev. 33, e17 (2018)

    Article  Google Scholar 

  4. Andrejczuk, E., Bistaffa, F., Blum, C., Rodríguez-Aguilar, J.A., Sierra, C.: Synergistic team composition: a computational approach to foster diversity in teams. Knowl.-Based Syst. 182, 104799 (2019)

    Article  Google Scholar 

  5. Bachrach, Y., Meir, R., Jung, K., Kohli, P.: Coalitional structure generation in skill games. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, , vol. 2, AAAI 2010, AtlantaGeorgia, USA, July 11–15 (2010)

    Google Scholar 

  6. Capezzuto, L., Tarapore, D., Ramchurn, S.: Anytime and efficient coalition formation with spatial and temporal constraints. In: Bassiliades, N., Chalkiadakis, G., de Jonge, D. (eds.) EUMAS/AT -2020. LNCS (LNAI), vol. 12520, pp. 589–606. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66412-1_38

    Chapter  Google Scholar 

  7. Chalkiadakis, G., Boutilier, C.: Sequentially optimal repeated coalition formation under uncertainty. Auton. Agent. Multi-agent Syst. 24(3), 441–484 (2012)

    Article  Google Scholar 

  8. Chalkiadakis, G., Elkind, E., Wooldridge, M.: Computational Aspects of Cooperative Game Theory (Synthesis Lectures on Artificial Inetlligence and Machine Learning), , 1st edn. Morgan & Claypool Publishers (2011)

    Google Scholar 

  9. Chevaleyre, Y., et al.: Issues in multiagent resource allocation. Informatica 30, 3–31 (2006)

    MATH  Google Scholar 

  10. Conitzer, V., Sandholm, T.: Complexity of manipulating elections with few candidates. In: Eighteenth National Conference on Artificial Intelligence, pp. 314–319. American Association for Artificial Intelligence, USA (2002)

    Google Scholar 

  11. Crawford, C., Rahaman, Z., Sen, S.: Evaluating the efficiency of robust team formation algorithms. In: Osman, N., Sierra, C. (eds.) Autonomous Agents and Multiagent Systems, pp. 14–29. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46882-2_2

    Chapter  Google Scholar 

  12. Czatnecki, E., Dutta, A.: Hedonic coalition formation for task allocation with heterogeneous robots. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 1024–1029 (2019)

    Google Scholar 

  13. ESCO: European skills, competences, qualifications and occupations. https://ec.europa.eu/esco/portal (2010)

  14. Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L.A., Rothe, J.: Llull and copeland voting broadly resist bribery and control. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, AAAI 2007, pp. 724–730. AAAI Press (2007)

    Google Scholar 

  15. Gamrath, G., et al.: The SCIP Optimization Suite 7.0. Technical report, Optimization Online (March 2020). www.optimization-online.org/DB_HTML/2020/03/7705.html

  16. Georgara, A., Rodríguez-Aguilar, J.A., Sierra, C.: Towards a Competence-Based Approach to Allocate Teams to Tasks, pp. 1504–1506. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2021)

    Google Scholar 

  17. Georgara, A., Rodríguez-Aguilar, J.A., et al.: An anytime heuristic algorithm for allocating many teams to many tasks. In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pp. 1598–1600. AAMAS ’22, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2022)

    Google Scholar 

  18. GLPK: Glpk: Gnu linear programming kit) (2018). www.gnu.org/software/glpk/

  19. GUROBI: Gurobi optimizer 8.0 (2018). www.gurobi.com/

  20. IBM: Ibm ilog cplex optimization studio 12.10 (2019). www.ibm.com/us-en/marketplace/ibm-ilog-cplex

  21. ISFOL: Professioni, occupazione, fabbisogni (2017). https://fabbisogni.isfol.it

  22. Kurtan, C., Yolum, P., Dastani, M.: An ideal team is more than a team of ideal agents. In: ECAI (2020)

    Google Scholar 

  23. Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2009, pp. 467–476. Association for Computing Machinery, New York, NY, USA (2009)

    Google Scholar 

  24. Li, Y., Bandar, Z.A., Mclean, D.: An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans. Knowl. Data Eng. 15(4), 871–882 (2003)

    Article  Google Scholar 

  25. Morrison, R.W., De Jong, K.A.: Measurement of population diversity. In: Collet, P., Fonlupt, C., Hao, J.K., Lutton, E., Schoenauer, M. (eds.) Artificial Evolution, pp. 31–41. Springer, Berlin (2002). https://doi.org/10.1007/3-540-46033-0_3

    Chapter  Google Scholar 

  26. Nguyen, T.T., Rothe, J.: Minimizing envy and maximizing average Nash social welfare in the allocation of indivisible goods. Discret. Appl. Math. 179, 54–68 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  27. Osman, N., Sierra, C., Mcneill, F., Pane, J., Debenham, J.: Trust and matching algorithms for selecting suitable agents. ACM Trans. Intell. Syst. Technol. 5(1), 16:1–16:39 (2014)

    Google Scholar 

  28. Ponda, S.S., Johnson, L.B., Geramifard, A., How, J.P.: Cooperative mission planning for multi-UAV teams. In: Valavanis, K.P., Vachtsevanos, G.J. (eds.) Handbook of Unmanned Aerial Vehicles, pp. 1447–1490. Springer, Dordrecht (2015). https://doi.org/10.1007/978-90-481-9707-1_16

    Chapter  Google Scholar 

  29. Präntare, F., Heintz, F.: An anytime algorithm for simultaneous coalition structure generation and assignment. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds.) PRIMA 2018. LNCS (LNAI), vol. 11224, pp. 158–174. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03098-8_10

    Chapter  Google Scholar 

  30. Präntare, F., Heintz, F.: An anytime algorithm for optimal simultaneous coalition structure generation and assignment. Auton. Agent. Multi-Agent Syst. 34(1), 1–31 (2020)

    Article  Google Scholar 

  31. Sa Silva, I.E., Krohling, R.A.: A fuzzy sociometric approach to human resource allocation. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8. IEEE (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence Research Institute, CSIC, Barcelona, Spain

    Athina Georgara, Juan A. Rodríguez-Aguilar & Carles Sierra

  2. Enzyme Advising Group, Barcelona, Spain

    Athina Georgara

Authors
  1. Athina Georgara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan A. Rodríguez-Aguilar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carles Sierra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Athina Georgara .

Editor information

Editors and Affiliations

  1. University of Düsseldorf, Düsseldorf, Germany

    Dorothea Baumeister

  2. University of Düsseldorf, Düsseldorf, Germany

    Jörg Rothe

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Georgara, A., A. Rodríguez-Aguilar, J., Sierra, C. (2022). Allocating Teams to Tasks: An Anytime Heuristic Competence-Based Approach. In: Baumeister, D., Rothe, J. (eds) Multi-Agent Systems. EUMAS 2022. Lecture Notes in Computer Science(), vol 13442. Springer, Cham. https://doi.org/10.1007/978-3-031-20614-6_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20614-6_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20613-9

  • Online ISBN: 978-3-031-20614-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation