TY - GEN
T1 - MAS Network
T2 - 22nd International Workshop on Multi-Agent-Based Simulation, MABS 2021
AU - Yamada, Hiroaki
AU - Shirahashi, Masataka
AU - Kamiyama, Naoyuki
AU - Nakajima, Yumeka
N1 - Funding Information:
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.
Funding Information:
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.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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 MASnetwork, 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.
AB - 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 MASnetwork, 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.
UR - http://www.scopus.com/inward/record.url?scp=85124122397&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-94548-0_9
DO - 10.1007/978-3-030-94548-0_9
M3 - Conference contribution
AN - SCOPUS:85124122397
SN - 9783030945473
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 113
EP - 124
BT - Multi-Agent-Based Simulation XXII - 22nd International Workshop, MABS 2021, Revised Selected Papers
A2 - Van Dam, Koen H.
A2 - Verstaevel, Nicolas
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 3 May 2021 through 7 May 2021
ER -