Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Adaptive Behavior Pub Date : 2022-08-26 DOI:10.1177/10597123221137090
A. Hepworth, Aya Hussein, D. Reid, H. Abbass
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引用次数: 5

Abstract

Contemporary swarm indicators are often used in isolation, focussed on extracting information at the individual or collective levels. Consequently, these are seldom integrated to infer a top-level operating picture of the swarm, its members and its overall collective dynamics. The primary contribution of this paper is to organise a suite of indicators about swarms into an ontologically arranged collection of information markers to characterise the swarm from the perspective of an external observer – , a recognition agent. Our contribution shows the foundations for a new area of research that we title swarm analytics, whose primary concern is with the design and organisation of collections of swarm markers to understand, detect, recognise, track and learn a particular insight about a swarm system. We present our designed framework of information markers that offer a new avenue for swarm research, especially for heterogeneous and cognitive swarms that may require more advanced capabilities to detect agencies and categorise agent influences and responses.
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群体分析:设计信息标记来描述牧羊环境中的群体系统
当代群体指标往往孤立地使用,侧重于在个人或集体层面提取信息。因此,很少将这些综合起来,以推断出群体、其成员及其整体集体动态的顶层操作情况。本文的主要贡献是将一套关于群体的指标组织成一个本体论上安排的信息标记集合,以从外部观察者(识别代理)的角度描述群体。我们的贡献显示了一个新的研究领域的基础,我们称之为群体分析,其主要关注的是群体标记集合的设计和组织,以理解,检测,识别,跟踪和学习关于群体系统的特定见解。我们提出了我们设计的信息标记框架,为群体研究提供了新的途径,特别是对于可能需要更先进的功能来检测代理和分类代理影响和响应的异构和认知群体。
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来源期刊
Adaptive Behavior
Adaptive Behavior 工程技术-计算机:人工智能
CiteScore
4.30
自引率
18.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: _Adaptive Behavior_ publishes articles on adaptive behaviour in living organisms and autonomous artificial systems. The official journal of the _International Society of Adaptive Behavior_, _Adaptive Behavior_, addresses topics such as perception and motor control, embodied cognition, learning and evolution, neural mechanisms, artificial intelligence, behavioral sequences, motivation and emotion, characterization of environments, decision making, collective and social behavior, navigation, foraging, communication and signalling. Print ISSN: 1059-7123
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