Abstract
RoboCupRescue Simulation is a large-scale multi-agent simulation of urban disasters where, in order to save lives and minimize damage, rescue teams must effectively cooperate despite sensing and communication limitations. This paper presents the comprehensive search and rescue approach of the ResQ Freiburg team, the winner in the RoboCupRescue Simulation league at RoboCup 2004.
Specific contributions include the predictions of travel costs and civilian life-time, the efficient coordination of an active disaster space exploration, as well as an any-time rescue sequence optimization based on a genetic algorithm.
We compare the performances of our team and others in terms of their capability of extinguishing fires, freeing roads from debris, disaster space exploration, and civilian rescue. The evaluation is carried out with information extracted from simulation log files gathered during RoboCup 2004. Our results clearly explain the success of our team, and also confirm the scientific approaches proposed in this paper.
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Homepage RoboCup Rescue 2005 (2005), http://kaspar.informatik.uni-freiburg.de/~rcr2005
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Communications of the ACM 18(9), 509–517 (1975)
Bock, H.H.: Autmatic Classification. Vandenhoeck and Ruprecht (1974)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth & Brooks (1984)
Drucker, H.: Improving regressors using boosting techniques. In: Proc. 14th International Conference on Machine Learning, pp. 107–115. Morgan Kaufmann, San Francisco (1997)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Holland, J.H.: Adaption in Natural and Artificial Systems. University of Michigan Press (1975)
Kitano, H., Tadokoro, S., Noda, I., Matsubara, H., Takahashi, T., Shinjou, A., Shimada, S.: RoboCup Rescue: Search and rescue in large-scale disasters as a domain for autonomous agents research. In: IEEE Conf. on Man, Systems, and Cybernetics (SMC 1999) (1999)
Kleiner, A., Brenner, M., Braeuer, T., Dornhege, C., Goebelbecker, M., Luber, M., Prediger, J., Stueckler, J., Nebel, B.: Resq freiburg: Team description and evaluation. Technical report, Institut für Informatik, Universität Freiburg, Germany (2005)
Kleiner, A., Goebelbecker, M.: Rescue3d: Making rescue simulation attractive to the public (2004), http://kaspar.informatik.uni-freiburg.de/~rescue3D/
Morimoto, T.: How to Develop a RoboCupRescue Agent (2002), http://ne.cs.uec.ac.jp/~morimoto/rescue/manual/
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco (2000)
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© 2006 Springer-Verlag Berlin Heidelberg
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Kleiner, A. et al. (2006). Successful Search and Rescue in Simulated Disaster Areas. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds) RoboCup 2005: Robot Soccer World Cup IX. RoboCup 2005. Lecture Notes in Computer Science(), vol 4020. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780519_29
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DOI: https://doi.org/10.1007/11780519_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35437-6
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