Autonomous Agents and Multi-Agent Systems

, Volume 20, Issue 3, pp 421–443 | Cite as

RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies

  • Paulo Roberto FerreiraJr.
  • Fernando dos Santos
  • Ana L. C. Bazzan
  • Daniel Epstein
  • Samuel J. Waskow
Article

Abstract

This paper addresses distributed task allocation among teams of agents in a RoboCup Rescue scenario. We are primarily concerned with testing different mechanisms that formalize issues underlying implicit coordination among teams of agents. These mechanisms are developed, implemented, and evaluated using two algorithms: Swarm-GAP and LA-DCOP. The latter bases task allocation on a comparison between an agent’s capability to perform a task and the capability demanded by this task. Swarm-GAP is a probabilistic approach in which an agent selects a task using a model inspired by task allocation among social insects. Both algorithms were also compared to another one that allocates tasks in a greedy way. Departing from previous works that tackle task allocation in the rescue scenario only among fire brigades, here we consider the various actors in the RoboCup Rescue, a step forward in the direction of realizing the concept of extreme teams. Tasks are allocated to teams of agents without explicit negotiation and using only local information. Our results show that the performance of Swarm-GAP and LA-DCOP are similar and that they outperform a greedy strategy. Also, it is possible to see that using more sophisticated mechanisms for task selection does pay off in terms of score.

Keywords

Task allocation Multiagent systems RoboCup Rescue DCOP GAP 

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References

  1. 1.
    Boffo, F., Ferreira, P. R., Jr., & Bazzan, A. L. C. (2007, December). A comparison of algorithms for task allocation in robocup rescue. In M. Dastani & R. H. Bordini (Ed.), Proceedings of the 5th European workshop on multiagent systems, pp. 537–548.Google Scholar
  2. 2.
    Bonabeau E., Theraulaz G., Dorigo M. (1999) Swarm intelligence: From natural to artificial systems. Oxford University Press, New York, USAMATHGoogle Scholar
  3. 3.
    Camazine S., Deneubourg J.D., Franks N.R., Sneyd J., Theraulaz G., Bonabeau E. (2003) Self-organization in biological systems. Princeton University Press, Princeton, N.J.MATHGoogle Scholar
  4. 4.
    dos Santos, F., & Bazzan, A. L. C. (2009). Ant-based task allocation among teams. In Proceedings of the eighth international joint conference on autonomous agents and multiagent systems. To appear.Google Scholar
  5. 5.
    Faltings B., Yokoo M. (2005) Introduction: Special issue on distributed constraint satisfaction. Artificial Intelligence 161: 1–5CrossRefMathSciNetGoogle Scholar
  6. 6.
    Farinelli, A., Iocchi, L., Nardi, D., & Ziparo, V. A. (2006, July). Assignment of dynamically perceived tasks by token passing in multirobot systems. Proceedings of the IEEE, 94(7), 1271–1288.Google Scholar
  7. 7.
    Ferreira, P. R., Jr., Boffo, F., & Bazzan, A. L. C. (2007, May). A swarm based approximated algorithm to the extended generalized assignment problem (E-GAP). In Proceedings of the sixth international joint conference on autonomous agents and multiagent systems (AAMAS), pp. 1231–1233.Google Scholar
  8. 8.
    Ferreira, P. R., Jr., Boffo, F., & Bazzan, A. L. C. (2008). Using swarm-GAP for distributed task allocation in complex scenarios. In N. Jamali, P. Scerri, & T. Sugawara, (Eds.), Massively multiagent systems, Number 5043 in lecture notes in artificial intelligence, pp. 107–121. Berlin:Springer.Google Scholar
  9. 9.
    Goldman C., Zilberstein S. (2004) Decentralized control of cooperative systems: Categorization and complexity analysis. Journal of Artificial Intelligence Research 22: 143–174MATHMathSciNetGoogle Scholar
  10. 10.
    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
  11. 11.
    Hara, T., & Toriumu, F. (2008, July). Robocup rescue 2008 repository—SUNTORI team.Google Scholar
  12. 12.
    Kalra, N., & Martinoli, A. (2006). A comparative study of market-based and threshold-based task allocation. Technical report, EPFL, Lausanne, Switzerland.Google Scholar
  13. 13.
    Karmani, R., Latvala, T., & Agha, G. (2007, July). On scaling multi-agent task reallocation using market-based approach. In Proceedings of the first IEEE international conference on self-adaptive and self-organizing systems, pp. 173–182.Google Scholar
  14. 14.
    Kitano, H., Tadokoro, S., Noda, I., Matsubara, H., Takahashi, T., Shinjou, A., & Shimada, S. (1999, October). 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.Google Scholar
  15. 15.
    Nair, R., Ito, T., Tambe, M., & Marsella, S. (2002). Task allocation in the rescue simulation domain: A short note. In A. Birk & S. Coradeschi (Ed.), RoboCup 2001: Robot Soccer World Cup V, Vol. 2377 of Lecture notes in computer science, pp. 751–754. Berlin: Springer-Verlag.Google Scholar
  16. 16.
    Nair, R., Tambe, M., Yokoo, M., Pynadath, D. V., & Marsella, S. (2003). Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings. In Proceedings of the eighteenth international joint conference on artificial intelligence (IJCAI-03), pp. 705–711, Acapulco, Mexico, August 9–15. Morgan Kaufmann.Google Scholar
  17. 17.
    Paquet S., Bernier N., Chaib-draa B. (2004) Comparison of different coordination strategies for the RoboCup Rescue simulation. In: Orchard B., Yang C., Ali M. (eds) Proceedings of the seventeenth international conference on industrial & engineering applications of artificial intelligence & expert systems. Springer, Ottawa, Canada. Berlin, pp 987–996Google Scholar
  18. 18.
    Paquet S., Chaib-draa B. (2006) Learning the required number of agents for complex tasks. In: Nakashima H., Wellman M.P., Weiss G., Stone P. (eds) Proceedings of the fifth international joint conference on autonomous agents and multiagent systems. ACM Press, Hakodate, Japan. New York, pp 736–746CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Shmoys, D.B., & Tardos, V. (1993). An approximation algorithm for the generalized assignment problem. Mathematical Programming, 62(3), 461–474.Google Scholar
  21. 21.
    Skinner, C., & Barley, M. (2006). Robocup rescue simulation competition: Status report. In A. Bredenfeld, A. Jacoff, I. Noda, & Y. Takahashi (Eds.), RoboCup 2005: Robot Soccer World Cup IX, Vol. 4020 of Lecture notes in computer science, pp. 632–639. Berlin: Springer-Verlag.Google Scholar
  22. 22.
    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, Vol. 265, pp. 327–332.Google Scholar
  23. 23.
    Yikun, T., Wang, Y., Zhong, S., Zhang, J., Wentong, L., & Baoping, H. (2008, July). Robocup rescue 2008 repository–ZJUBase team.Google Scholar
  24. 24.
    Zhang W., Wittenburg L. (2002) Distributed breakout revisited. In: Dechter R., Kearns M., Sutton R. (eds) Eighteenth national conference on artificial intelligence. American Association for Artificial Intelligence, Menlo Park, CA, USA, pp 352–357Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Paulo Roberto FerreiraJr.
    • 1
    • 2
  • Fernando dos Santos
    • 1
  • Ana L. C. Bazzan
    • 1
  • Daniel Epstein
    • 1
  • Samuel J. Waskow
    • 1
  1. 1.Instituto de InformáticaUniversidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil
  2. 2.Instituto de Física e Matemática, Departamento de InformáticaUniversidade Federal de PelotasPelotasBrazil

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