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

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Abstract

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.

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References

  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.

  2. Bonabeau E., Theraulaz G., Dorigo M. (1999) Swarm intelligence: From natural to artificial systems. Oxford University Press, New York, USA

    MATH  Google Scholar 

  3. Cicirello V., Smith S. (2004) Wasp-like agents for distributed factory coordination. Journal of Autonomous Agents and Multi-Agent Systems 8(3): 237–266

    Article  Google Scholar 

  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 107–121

    Google Scholar 

  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): 421–443

    Article  Google Scholar 

  6. Ham M., Agha G. (2007) Market-based coordination strategies for large-scale multi-agent systems. System and Information Sciences Notes 2(1): 126–131

    Google Scholar 

  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 43–63

    Google Scholar 

  8. Hölldobler B. (1983) Territorial behavior in the green tree ant (Oecophylla smaragdina). Biotropica 15(4): 241–250

    Article  Google Scholar 

  9. Hölldobler B., Stanton R. C., Markl H. (1978) Recruitment and food-retrieving behavior in Novomessor (formicidae, hymenoptera). Behavioral Ecology and Sociobiology 4(2): 163–181

    Article  Google Scholar 

  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.

  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.

  12. Krieger M. J. B., Billeter J.-B., Keller L. (2000) Ant-like task allocation and recruitment in cooperative robots. Nature 406(6799): 992–995

    Article  Google Scholar 

  13. Kube R., Bonabeau E. (2000) Cooperative transport by ants and robots. Robotics and Autonomous Systems 30(1/2): 85–101 ISSN: 0921-8890

    Article  Google Scholar 

  14. Rahwan T., Jennings N. R. (2007) An algorithm for distributing coalitional value calculations among cooperating agents. Artificial Intelligence Journal 171(8–9): 535–567

    Article  MATH  MathSciNet  Google Scholar 

  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): 1–22

    Article  Google Scholar 

  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.

  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 727–734

    Chapter  Google Scholar 

  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.

  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 632–639

    Chapter  Google Scholar 

  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): 1104–1113

    Article  Google Scholar 

  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.

  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.

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Authors and Affiliations

  1. PPGC—Universidade Federaldo Rio Grande do Sul, Caixa Postal 15064, Porto Alegre, RS, 91.501-970, Brazil

    Fernando dos Santos & Ana L. C. Bazzan

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  1. Fernando dos Santos
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  2. Ana L. C. Bazzan
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Correspondence to Ana L. C. Bazzan.

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dos Santos, F., Bazzan, A.L.C. Towards efficient multiagent task allocation in the RoboCup Rescue: a biologically-inspired approach. Auton Agent Multi-Agent Syst 22, 465–486 (2011). https://doi.org/10.1007/s10458-010-9136-3

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