MAS Network: Surrogate Neural Network for Multi-agent Simulation

Hiroaki Yamada, Masataka Shirahashi, Naoyuki Kamiyama, Yumeka Nakajima

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

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.

本文言語英語
ホスト出版物のタイトルMulti-Agent-Based Simulation XXII - 22nd International Workshop, MABS 2021, Revised Selected Papers
編集者Koen H. Van Dam, Nicolas Verstaevel
出版社Springer Science and Business Media Deutschland GmbH
ページ113-124
ページ数12
ISBN(印刷版)9783030945473
DOI
出版ステータス出版済み - 2022
イベント22nd International Workshop on Multi-Agent-Based Simulation, MABS 2021 - Virtual, Online
継続期間: 5月 3 20215月 7 2021

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13128 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議22nd International Workshop on Multi-Agent-Based Simulation, MABS 2021
CityVirtual, Online
Period5/3/215/7/21

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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