Towards efficient multiagent task allocation in the RoboCup Rescue: a biologically-inspired approach
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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.
KeywordsOptimisation in multiagent systems Task allocation Robocup Rescue Swarm intelligence
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- 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
- 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
- 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.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
- 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.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
- 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.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.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
- 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.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