Autonomous Agents and Multi-Agent Systems

, Volume 22, Issue 3, pp 465–486 | Cite as

Towards efficient multiagent task allocation in the RoboCup Rescue: a biologically-inspired approach

  • Fernando dos Santos
  • Ana L. C. BazzanEmail author


This paper addresses team formation in the RoboCup Rescue centered on task allocation. We follow a previous approach that is based on so-called extreme teams, which have four key characteristics: agents act in domains that are dynamic; agents may perform multiple tasks; agents have overlapping functionality regarding the execution of each task but differing levels of capability; and some tasks may depict constraints such as simultaneous execution. So far these four characteristics have not been fully tested in domains such as the RoboCup Rescue. We use a swarm intelligence based approach, address all characteristics, and compare it to other two GAP-based algorithms. Experiments where computational effort, communication load, and the score obtained in the RoboCup Rescue aremeasured, show that our approach outperforms the others.


Optimisation in multiagent systems Task allocation Robocup Rescue Swarm intelligence 


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  1. 1.
    Agassounon, W., & Martinoli, A. (2002). Efficiency and robustness of threshold-based distributed allocation algorithms in multi-agent systems. In Proceedings of the first international joint conference on autonomous agents and multiagent systems, AAMAS 2002 (pp. 1090–1097). New York, NY, USA: ACM.Google Scholar
  2. 2.
    Bonabeau E., Theraulaz G., Dorigo M. (1999) Swarm intelligence: From natural to artificial systems. Oxford University Press, New York, USAzbMATHGoogle Scholar
  3. 3.
    Cicirello V., Smith S. (2004) Wasp-like agents for distributed factory coordination. Journal of Autonomous Agents and Multi-Agent Systems 8(3): 237–266CrossRefGoogle Scholar
  4. 4.
    Ferreira P. R. Jr., Boffo F., Bazzan A. L. C. (2008) Using swarm-GAP for distributed task allocation in complex scenarios. In: Jamali N., Scerri P., Sugawara T. (eds) Massively multiagent systems, volume of 5043 in lecture notes in artificial intelligence. Springer, Berlin, pp 107–121Google Scholar
  5. 5.
    Ferreira P. R. Jr., dos Santos F., Bazzan A. L. C., Epstein D., Waskow S. J. (2010) Robocup Rescue as multiagent task allocation among teams: Experiments with task interdependencies. Journal of Autonomous Agents and Multiagent Systems 20(3): 421–443CrossRefGoogle Scholar
  6. 6.
    Ham M., Agha G. (2007) Market-based coordination strategies for large-scale multi-agent systems. System and Information Sciences Notes 2(1): 126–131Google Scholar
  7. 7.
    Ham M., Agha G. (2008) A study of coordinated dynamic market-based task assignment in massively multi-agent systems. In: Jamali N., Scerri P., Sugawara T. (eds) Massively multiagent systems, volume of 5043 in lecture notes in artificial intelligence. Springer, Berlin, pp 43–63Google Scholar
  8. 8.
    Hölldobler B. (1983) Territorial behavior in the green tree ant (Oecophylla smaragdina). Biotropica 15(4): 241–250CrossRefGoogle Scholar
  9. 9.
    Hölldobler B., Stanton R. C., Markl H. (1978) Recruitment and food-retrieving behavior in Novomessor (formicidae, hymenoptera). Behavioral Ecology and Sociobiology 4(2): 163–181CrossRefGoogle Scholar
  10. 10.
    Hunsberger, L., & Grosz, B. J. (2000). A combinatorial auction for collaborative planning. In Proceedings of the fourth international conference on multiAgent aystems, ICMAS (pp. 151–158). Boston.Google Scholar
  11. 11.
    Kitano, H., Tadokoro, S., Noda, I., Matsubara, H., Takahashi, T., Shinjou, A., & Shimada, S. (1999). Robocup Rescue: Search and rescue in large-scale disasters as adomain for autonomous agents research. In Proceedings of the IEEE international conference on systems, man, and cybernetics, SMC (Vol. 6, pp. 739–743) Tokyo, Japan: IEEE.Google Scholar
  12. 12.
    Krieger M. J. B., Billeter J.-B., Keller L. (2000) Ant-like task allocation and recruitment in cooperative robots. Nature 406(6799): 992–995CrossRefGoogle Scholar
  13. 13.
    Kube R., Bonabeau E. (2000) Cooperative transport by ants and robots. Robotics and Autonomous Systems 30(1/2): 85–101 ISSN: 0921-8890CrossRefGoogle Scholar
  14. 14.
    Rahwan T., Jennings N. R. (2007) An algorithm for distributing coalitional value calculations among cooperating agents. Artificial Intelligence Journal 171(8–9): 535–567CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Robson S. K., Traniello J. F. A. (1998) Resource assessment, recruitment behavior, and organization of cooperative prey retrieval in the ant Formica schaufussi (hymenoptera: Formicidae). Journal of Insect Behavior 11(1): 1–22CrossRefGoogle Scholar
  16. 16.
    Santos, F. D., & Bazzan, A. L. C. (2009). eXtreme-ants: Ant based algorithm for task allocation in extreme teams. In N. R. Jennings, A. Rogers, J. A. R. Aguilar, A. Farinelli, & S. D. Ramchurn (Eds.), Proceedings of the second international workshop on optimisation in multi-agent systems (pp. 1–8). Budapest, Hungary, May.Google Scholar
  17. 17.
    Scerri P., Farinelli A., Okamoto S., Tambe M. (2005) Allocating tasks in extreme teams. In: Dignum F., Dignum V., Koenig S., Kraus S., Singh M.P., Wooldridge M. (eds) Proceedings of the fourth international joint conference on autonomous agents and multiagent systems. ACM Press, New York USA, pp 727–734CrossRefGoogle Scholar
  18. 18.
    Shehory, O., & Kraus, S. (1995). Task allocation via coalition formation among autonomous agents. In Proceedings of the fourteenth international joint conference on artificial intelligence (pp. 655–661). Montréal, Canada: Morgan Kaufmann.Google Scholar
  19. 19.
    Skinner C., Barley M. (2006) Robocup Rescue simulation competition: Status report. In: Bredenfeld A., Jacoff A., Noda I., Takahashi Y. (eds) RoboCup 2005: Robot Soccer world cup IX, volume of 4020 in lecture notes in computer science. Springer, Berlin, pp 632–639CrossRefGoogle Scholar
  20. 20.
    Smith R.G. (1980) The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers C-29(12): 1104–1113CrossRefGoogle Scholar
  21. 21.
    Theraulaz, G., Bonabeau, E., & Deneubourg, J. (1998). Response threshold reinforcement and division of labour in insect societies. In Royal Society of London Series B - Biological Sciences, 265, 327–332.Google Scholar
  22. 22.
    Xu, Y., Scerri, P., Sycara, K., & Lewis, M. (2006). Comparing market and token-based coordination. In Proceedings of the fifth international joint conference on autonomous agents and multiagent systems, AAMAS 2006 (pp. 1113–1115). New York, NY, USA: ACM.Google Scholar

Copyright information

© The Author(s) 2010

Authors and Affiliations

  1. 1.PPGC—Universidade Federaldo Rio Grande do SulPorto AlegreBrazil

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