A Simulation Environment for the Neuroevolution of Ant Colony Dynamics

Michael Crosscombe, Ilya Horiguchi, Norihiro Maruyama, Shigeto Dobata, Takashi Ikegami
{"title":"A Simulation Environment for the Neuroevolution of Ant Colony Dynamics","authors":"Michael Crosscombe, Ilya Horiguchi, Norihiro Maruyama, Shigeto Dobata, Takashi Ikegami","doi":"arxiv-2406.13147","DOIUrl":null,"url":null,"abstract":"We introduce a simulation environment to facilitate research into emergent\ncollective behaviour, with a focus on replicating the dynamics of ant colonies.\nBy leveraging real-world data, the environment simulates a target ant trail\nthat a controllable agent must learn to replicate, using sensory data observed\nby the target ant. This work aims to contribute to the neuroevolution of models\nfor collective behaviour, focusing on evolving neural architectures that encode\ndomain-specific behaviours in the network topology. By evolving models that can\nbe modified and studied in a controlled environment, we can uncover the\nnecessary conditions required for collective behaviours to emerge. We hope this\nenvironment will be useful to those studying the role of interactions in\nemergent behaviour within collective systems.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Adaptation and Self-Organizing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.13147","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

We introduce a simulation environment to facilitate research into emergent collective behaviour, with a focus on replicating the dynamics of ant colonies. By leveraging real-world data, the environment simulates a target ant trail that a controllable agent must learn to replicate, using sensory data observed by the target ant. This work aims to contribute to the neuroevolution of models for collective behaviour, focusing on evolving neural architectures that encode domain-specific behaviours in the network topology. By evolving models that can be modified and studied in a controlled environment, we can uncover the necessary conditions required for collective behaviours to emerge. We hope this environment will be useful to those studying the role of interactions in emergent behaviour within collective systems.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
蚁群动力学神经进化模拟环境
通过利用真实世界的数据,该环境模拟了目标蚂蚁的活动轨迹,可控代理必须利用目标蚂蚁观察到的感官数据,学习复制目标蚂蚁的活动轨迹。这项工作旨在为集体行为模型的神经进化做出贡献,重点是在网络拓扑中编码特定领域行为的神经架构的进化。通过进化可在受控环境中修改和研究的模型,我们可以发现集体行为出现所需的必要条件。我们希望这个环境能对研究集体系统中交互作用在萌发行为中的作用的人有所帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Expected and unexpected routes to synchronization in a system of swarmalators Synchronization cluster bursting in adaptive oscillators networks The forced one-dimensional swarmalator model Periodic systems have new classes of synchronization stability Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: A dynamical system vs. machine learning approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1