Abstract
We present a research environment for combinatorial experiments for the RoboCupRescue Simulation, which is a platform for the study of disaster-relief strategies using multi-agent simulations. To simulate the agents in disaster-relief situations in the RoboCupRescue Simulation, it is necessary to implement a wide variety of algorithms for tasks such as such as group formation, path planning, and task allocation. Recently, we proposed a modular framework, the Agent Development Framework, that enables researchers to implement, study, and test each algorithm independently. Because the algorithms developed in this framework are mutually replaceable, it is possible to combine algorithms developed by different researchers. In this study, we further propose an experimental environment to efficiently handle the experiments of a huge number of possible combinations of the algorithms. As a demonstration, we test various combinations of the algorithms developed by the participants of RoboCup 2017 and show that there indeed exists a set of the algorithms that is superior to the original ones developed by each team.
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Acknowledgment
This work was supported by JSPS KAKENHI Grant Number JP16K00310 and JP17K00317. This work was partially supported by MEXT Post-K project “Studies of multi-level spatiotemporal simulation of socioeconomic phenomena”. We thank Kimberly Moravec, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.
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Takami, S., Onishi, M., Iwata, K., Ito, N., Murase, Y., Uchitane, T. (2018). An Environment for Combinatorial Experiments in a Multi-agent Simulation for Disaster Response. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_52
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