{"title":"Contextually aware intelligent control agents for heterogeneous swarms","authors":"","doi":"10.1007/s11721-024-00235-w","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain simplicity in their decision models, whilst increasing the swarm’s abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm control intelligent agent (shepherd). We first use swarm metrics to recognise the type of swarm that the shepherd interacts with, then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. contents) of the control agent without sacrificing the low computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.</p>","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"217 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11721-024-00235-w","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain simplicity in their decision models, whilst increasing the swarm’s abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm control intelligent agent (shepherd). We first use swarm metrics to recognise the type of swarm that the shepherd interacts with, then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. contents) of the control agent without sacrificing the low computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.
期刊介绍：
Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research
and developments in the multidisciplinary field of swarm intelligence. The journal publishes
original research articles and occasional review articles on theoretical, experimental and/or
practical aspects of swarm intelligence. All articles are published both in print and in electronic
form. There are no page charges for publication. Swarm Intelligence is published quarterly.
The field of swarm intelligence deals with systems composed of many individuals that coordinate
using decentralized control and self-organization. In particular, it focuses on the collective
behaviors that result from the local interactions of the individuals with each other and with their
environment. It is a fast-growing field that encompasses the efforts of researchers in multiple
disciplines, ranging from ethology and social science to operations research and computer
engineering.
Swarm Intelligence will report on advances in the understanding and utilization of swarm
intelligence systems, that is, systems that are based on the principles of swarm intelligence. The
following subjects are of particular interest to the journal:
• modeling and analysis of collective biological systems such as social insect colonies, flocking
vertebrates, and human crowds as well as any other swarm intelligence systems;
• application of biological swarm intelligence models to real-world problems such as distributed
computing, data clustering, graph partitioning, optimization and decision making;
• theoretical and empirical research in ant colony optimization, particle swarm optimization,
swarm robotics, and other swarm intelligence algorithms.
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