MAS Network: Surrogate Neural Network for Multi-agent Simulation

Hiroaki Yamada, Masataka Shirahashi, Naoyuki Kamiyama, Yumeka Nakajima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 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.

Original languageEnglish
Title of host publicationMulti-Agent-Based Simulation XXII - 22nd International Workshop, MABS 2021, Revised Selected Papers
EditorsKoen H. Van Dam, Nicolas Verstaevel
PublisherSpringer Science and Business Media Deutschland GmbH
Pages113-124
Number of pages12
ISBN (Print)9783030945473
DOIs
Publication statusPublished - 2022
Event22nd International Workshop on Multi-Agent-Based Simulation, MABS 2021 - Virtual, Online
Duration: May 3 2021May 7 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13128 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Workshop on Multi-Agent-Based Simulation, MABS 2021
CityVirtual, Online
Period5/3/215/7/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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