Swarm Intelligence最新文献_第5页

ANTS 2020 Special Issue: Editorial 《蚂蚁2020》特刊:社论

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-11-23 DOI: 10.1007/s11721-021-00208-3

M. Dorigo, T. Stützle, M. Blesa, C. Blum, Heiko Hamann, Mary Katherine Heinrich

引用次数: 0

A machine education approach to swarm decision-making in best-of-n problems 一种机器教育方法在最优化问题中进行群体决策

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-11-22 DOI: 10.1007/s11721-021-00206-5

Hussein, Aya, Elsawah, Sondoss, Petraki, Eleni, Abbass, Hussein A.

{"title":"A machine education approach to swarm decision-making in best-of-n problems","authors":"Hussein, Aya, Elsawah, Sondoss, Petraki, Eleni, Abbass, Hussein A.","doi":"10.1007/s11721-021-00206-5","DOIUrl":"https://doi.org/10.1007/s11721-021-00206-5","url":null,"abstract":"In swarm decision making, hand-crafting agents’ rules that use local information to achieve desirable swarm-level behaviours is a non-trivial design problem. Instead of relying entirely on swarm experts for designing these local rules, machine learning (ML) algorithms can be utilised for learning some of the local rules by mapping an agent’s perception to an appropriate action. To facilitate this process, we propose the use of Machine Education (ME) as a systematic approach for designing a curriculum for teaching the agents the required skills to autonomously select appropriate behaviours. We study the use of ME in the context of decision-making in best-of-n problems. The proposed approach draws on swarm robotics expertise for identifying agents’ capabilities and limitations, the skills required for generating the desirable behaviours, and the corresponding performance measures. In addition, ME utilises ML expertise for the selection and development of the ML algorithms suitable for each skill. The results of the experimental evaluations demonstrate the superior efficacy of the ME-based approach over the state-of-the-art approaches with respect to speed and accuracy. In addition, our approach shows considerable robustness to changes in swarm size and to changes in sensing and communication noise. Our findings promote the use of ME for teaching swarm members the required skills for achieving complex swarm tasks.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"348 ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138505612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Ant colony optimization for feasible scheduling of step-controlled smart grid generation 步进控制智能电网发电可行调度的蚁群优化

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-10-19 DOI: 10.1007/s11721-021-00204-7

Jörg Bremer, S. Lehnhoff

引用次数: 0

Reinforcement learning as a rehearsal for swarm foraging 强化学习作为群体觅食的预演

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-09-29 DOI: 10.1007/s11721-021-00203-8

Nguyen, Trung, Banerjee, Bikramjit

引用次数: 1

Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing 基于分布式贝叶斯信念共享的群机器人离散集合估计

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-09-05 DOI: 10.1007/s11721-021-00201-w

Qihao Shan, Sanaz Mostaghim

引用次数: 11

Collective decision-making for dynamic environments with visual occlusions 具有视觉遮挡的动态环境下的集体决策

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-08-25 DOI: 10.1007/s11721-021-00200-x

Fan Jiang, Hui Cheng, Guanrong Chen

引用次数: 0

HuGoS: a virtual environment for studying collective human behavior from a swarm intelligence perspective HuGoS：从群体智能角度研究人类集体行为的虚拟环境

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-08-03 DOI: 10.1007/s11721-021-00199-1

Nicolas Coucke, Mary Katherine Heinrich, A. Cleeremans, M. Dorigo

引用次数: 6

Achieving task allocation in swarm intelligence with bi-objective embodied evolution 基于双目标嵌入进化的群体智能任务分配

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-07-04 DOI: 10.1007/s11721-021-00198-2

Qihao Shan, Sanaz Mostaghim

引用次数: 1

Collective preference learning in the best-of-n problem 最优化问题中的集体偏好学习

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-06-02 DOI: 10.1007/s11721-021-00191-9

Michael Crosscombe, Jonathan Lawry

{"title":"Collective preference learning in the best-of-n problem","authors":"Michael Crosscombe, Jonathan Lawry","doi":"10.1007/s11721-021-00191-9","DOIUrl":"https://doi.org/10.1007/s11721-021-00191-9","url":null,"abstract":"Decentralised autonomous systems rely on distributed learning to make decisions and to collaborate in pursuit of a shared objective. For example, in swarm robotics the best-of-n problem is a well-known collective decision-making problem in which agents attempt to learn the best option out of n possible alternatives based on local feedback from the environment. This typically involves gathering information about all n alternatives while then systematically discarding information about all but the best option. However, for applications such as search and rescue in which learning the ranking of options is useful or crucial, best-of-n decision-making can be wasteful and costly. Instead, we investigate a more general distributed learning process in which agents learn a preference ordering over all of the n options. More specifically, we introduce a distributed rank learning algorithm based on three-valued logic. We then use agent-based simulation experiments to demonstrate the effectiveness of this model. In this context, we show that a population of agents are able to learn a total ordering over the n options and furthermore the learning process is robust to evidential noise. To demonstrate the practicality of our model, we restrict the communication bandwidth between the agents and show that this model is also robust to limited communications whilst outperforming a comparable probabilistic model under the same communication conditions.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"326 ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138505592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Quorum sensing without deliberation: biological inspiration for externalizing computation to physical spaces in multi-robot systems 未经审议的群体感应:在多机器人系统中将计算外部化到物理空间的生物学启示

IF 2.6 4区计算机科学

Swarm Intelligence Pub Date : 2021-06-01 DOI: 10.1007/s11721-021-00196-4

Theodore P. Pavlic, J. Hanson, Gabriele Valentini, S. Walker, S. Pratt

引用次数: 2