Abstract
Multi-agent simulation (MAS) plays an important role in analyzing our societies because it can model complexity in societies and assimilate a variety of social data. However, the execution of MAS is computationally expensive. When running numerous executions to determine optimal policy, it is crucial to develop a more computationally efficient mathematical model that is able to sufficiently substitute for the original simulation. In this paper, we propose a machine learning framework for developing neural network models, called \( {MAS\ network}\), that can substitute for MAS. Furthermore, we propose an effective feature representation of agent parameters and a systematic dataset design for learning. We confirmed that the MAS network replicated the system dynamics of the simulation and that the MAS network accurately learned the sensitivity of output and input relation even at unknown parameter points.
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Acknowledgments
This work was supported by Fujitsu Laboratories Ltd. Computational resources of AI Bridging Cloud Infrastructure (ABCI) provided by National Institute of the Advanced Industrial Science and Technology (AIST) were used.
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Yamada, H., Shirahashi, M., Kamiyama, N., Nakajima, Y. (2022). MAS Network: Surrogate Neural Network for Multi-agent Simulation. In: Van Dam, K.H., Verstaevel, N. (eds) Multi-Agent-Based Simulation XXII. MABS 2021. Lecture Notes in Computer Science(), vol 13128. Springer, Cham. https://doi.org/10.1007/978-3-030-94548-0_9
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DOI: https://doi.org/10.1007/978-3-030-94548-0_9
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