STRATA: unified framework for task assignments in large teams of heterogeneous agents


Large teams of heterogeneous agents have the potential to solve complex multi-task problems that are intractable for a single agent working independently. However, solving complex multi-task problems requires leveraging the relative strengths of the different kinds of agents in the team. We present Stochastic TRAit-based Task Assignment (STRATA), a unified framework that models large teams of heterogeneous agents and performs effective task assignments. Specifically, given information on which traits (capabilities) are required for various tasks, STRATA computes the assignments of agents to tasks such that the trait requirements are achieved. Inspired by prior work in robot swarms and biodiversity, we categorize agents into different species (groups) based on their traits. We model each trait as a continuous variable and differentiate between traits that can and cannot be aggregated from different agents. STRATA is capable of reasoning about both species-level and agent-level variability in traits. Further, we define measures of diversity for any given team based on the team’s continuous-space trait model. We illustrate the necessity and effectiveness of STRATA using detailed experiments based in simulation and in a capture-the-flag game environment.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. 1.

    Similar to prior work in multi-robot systems [28], we use the term “species” to describe a group of agents with similar traits. This does not imply any similarity to biological species.

  2. 2.

    We drop the arguments of \(\mathrm {Var}_Y\) for brevity.

  3. 3.

    Source code acquired from


  1. 1.

    Albrecht, S. V., & Stone, P. (2018). Autonomous agents modelling other agents: A comprehensive survey and open problems. Artificial Intelligence, 258, 66–95.

    MathSciNet  Article  Google Scholar 

  2. 2.

    Atamtürk, A., Nemhauser, G. L., & Savelsbergh, M. W. (1996). A combined lagrangian, linear programming, and implication heuristic for large-scale set partitioning problems. Journal of Heuristics, 1(2), 247–259.

    Article  Google Scholar 

  3. 3.

    Bandyopadhyay, S., Chung, S. J., & Hadaegh, F. Y. (2017). Probabilistic and distributed control of a large-scale swarm of autonomous agents. IEEE Transactions on Robotics, 33(5), 1103–1123.

    Article  Google Scholar 

  4. 4.

    Beni, G. (2004). From swarm intelligence to swarm robotics. In International Workshop on Swarm Robotics (pp. 1–9). Berlin: Springer.

  5. 5.

    Berman, S., Halász, Á., Hsieh, M. A., & Kumar, V. (2009). Optimized stochastic policies for task allocation in swarms of robots. IEEE Transactions on Robotics, 25(4), 927–937.

    Article  Google Scholar 

  6. 6.

    Bordini, R. H., Dastani, M., Dix, J., & Seghrouchni, A. E. F. (2007). Programming multi-agent-systems. In 4th international workshop, revised and invited papers (Vol. 4411). New York: Springer.

  7. 7.

    Cohen, L., Uras, T., Kumar, T.S., Xu, H., Ayanian, N., & Koenig, S. (2016) Improved solvers for bounded-suboptimal multi-agent path finding. In International joint conferences on artificial intelligence (IJCAI) (pp. 3067–3074)

  8. 8.

    DeCostanza, A. H., Marathe, A. R., Bohannon, A., Evans, A. W., Palazzolo, E. T., Metcalfe, J. S., et al. (2018). Enhancing human-agent teaming with individualized, adaptive technologies: A discussion of critical scientific questions. Technical report, US Army Research Laboratory Aberdeen Proving Ground United States.

  9. 9.

    Dias, M. B., Zlot, R., Kalra, N., & Stentz, A. (2006). Market-based multirobot coordination: A survey and analysis. Proceedings of the IEEE, 94(7), 1257–1270.

    Article  Google Scholar 

  10. 10.

    Gerkey, B. P., & Matarić, M. J. (2004). A formal analysis and taxonomy of task allocation in multi-robot systems. The International Journal of Robotics Research, 23(9), 939–954.

    Article  Google Scholar 

  11. 11.

    Guerrero, J., & Oliver, G. (2003). Multi-robot task allocation strategies using auction-like mechanisms. Artificial Research and Development in Frontiers in Artificial Intelligence and Applications, 100, 111–122.

    Google Scholar 

  12. 12.

    Hoffman, K. L., & Padberg, M. (1993). Solving airline crew scheduling problems by branch-and-cut. Management Science, 39(6), 657–682.

    Article  Google Scholar 

  13. 13.

    Hsieh, M. A., Halász, Á., Berman, S., & Kumar, V. (2008). Biologically inspired redistribution of a swarm of robots among multiple sites. Swarm Intelligence, 2(2–4), 121–141.

    Article  Google Scholar 

  14. 14.

    Jang, I., Shin, H. S., & Tsourdos, A. (2018a). Anonymous hedonic game for task allocation in a large-scale multiple agent system. IEEE Transactions on Robotics, 34(6), 1534–1548.

    Article  Google Scholar 

  15. 15.

    Jang, I., Shin, H. S., & Tsourdos, A. (2018b). Local information-based control for probabilistic swarm distribution guidance. Swarm Intelligence, 12(4), 327–359.

    Article  Google Scholar 

  16. 16.

    Kalbfleisch, J., & Lawless, J. F. (1985). The analysis of panel data under a markov assumption. Journal of the American Statistical Association, 80(392), 863–871.

    MathSciNet  Article  Google Scholar 

  17. 17.

    Khamis, A., Hussein, A., & Elmogy, A. (2015). Multi-robot task allocation: A review of the state-of-the-art. In Cooperative robots and sensor networks 2015 (pp. 31–51). Berlin; Springer

  18. 18.

    Korsah, G. A., Stentz, A., & Dias, M. B. (2013). A comprehensive taxonomy for multi-robot task allocation. The International Journal of Robotics Research, 32(12), 1495–1512.

    Article  Google Scholar 

  19. 19.

    Li, J., Esteban-Fernàndez de Àvila, B., Gao, W., Zhang, L., & Wang, J. (2017). Micro/nanorobots for biomedicine: Delivery, surgery, sensing, and detoxification. Science Robotics.

    Article  Google Scholar 

  20. 20.

    Lin, L., & Zheng, Z. (2005). Combinatorial bids based multi-robot task allocation method. In IEEE international conference on robotics and automation (ICRA), IEEE (pp. 1145–1150).

  21. 21.

    Ma, H., & Koenig, S. (2016). Optimal target assignment and path finding for teams of agents. In International conference on autonomous agents & Multiagent Systems (AAMAS), international foundation for autonomous agents and multiagent systems (pp. 1144–1152).

  22. 22.

    MacAlpine, P., Price, E., & Stone, P. (2015). SCRAM: Scalable collision-avoiding role assignment with minimal-makespan for formational positioning. In AAAI conference on artificial intelligence.

  23. 23.

    Matthey, L., Berman, S., & Kumar, V. (2009). Stochastic strategies for a swarm robotic assembly system. In International conference on robotics and automation (ICRA), IEEE (pp. 1953–1958).

  24. 24.

    Milam, M. B., Franz, R., Hauser, J. E., & Murray, R. M. (2005). Receding horizon control of vectored thrust flight experiment. IEE Proceedings-Control Theory and Applications, 152(3), 340–348.

    Article  Google Scholar 

  25. 25.

    Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233.

    Article  Google Scholar 

  26. 26.

    Parker, L. E., & Tang, F. (2006). Building multirobot coalitions through automated task solution synthesis. Proceedings of the IEEE, 94(7), 1289–1305.

    Article  Google Scholar 

  27. 27.

    Petchey, O. L., & Gaston, K. J. (2002). Functional diversity (fd), species richness and community composition. Ecology Letters, 5(3), 402–411.

    Article  Google Scholar 

  28. 28.

    Prorok, A., Hsieh, M. A., & Kumar, V. (2017). The impact of diversity on optimal control policies for heterogeneous robot swarms. IEEE Trans Robotics, 33(2), 346–358.

    Article  Google Scholar 

  29. 29.

    Shehory, O., & Kraus, S. (1995). A kernel-oriented model for autonomous-agent coalition-formation in general environments. In Australian workshop on distributed artificial intelligence (pp. 31–45). Berlin: Springer.

  30. 30.

    Shehory, O., & Kraus, S. (1998). Methods for task allocation via agent coalition formation. Artificial Intelligence, 101(1), 165–200.

    MathSciNet  Article  Google Scholar 

  31. 31.

    Shkurti, F., Xu, A., Meghjani, M., Higuera, J. C. G., Girdhar, Y., Giguere, P., et al. (2012). Multi-domain monitoring of marine environments using a heterogeneous robot team. In International conference on intelligent robots and systems (IROS), IEEE/RSJ (pp. 1747–1753).

  32. 32.

    Tokekar, P., Vander Hook, J., Mulla, D., & Isler, V. (2016). Sensor planning for a symbiotic uav and UGV system for precision agriculture. IEEE Transactions on Robotics, 32(6), 1498–1511.

    Article  Google Scholar 

  33. 33.

    Vail, D., & Veloso, M. (2003). Multi-robot dynamic role assignment and coordination through shared potential fields. Multi-robot Systems, 2, 87–98.

    Google Scholar 

  34. 34.

    Vig, L., & Adams, J. A. (2006). Multi-robot coalition formation. IEEE Transactions on Robotics, 22(4), 637–649.

    Article  Google Scholar 

  35. 35.

    Werfel, J., Petersen, K., & Nagpal, R. (2014). Designing collective behavior in a termite-inspired robot construction team. Science, 343(6172), 754–758.

    Article  Google Scholar 

  36. 36.

    Wurman, P. R., D’Andrea, R., & Mountz, M. (2008). Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Magazine, 29(1), 9.

    Google Scholar 

Download references


We would like to thank the anonymous reviews for their constructive feedback that greatly helped improve the quality of this article. This work was supported by the Army Research Lab under Grant W911NF-17-2-0181 (DCIST CRA).

Author information



Corresponding author

Correspondence to Harish Ravichandar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ravichandar, H., Shaw, K. & Chernova, S. STRATA: unified framework for task assignments in large teams of heterogeneous agents. Auton Agent Multi-Agent Syst 34, 38 (2020).

Download citation


  • Multi-agent systems
  • Task assignment
  • Heterogeneous agents