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交互半径如何影响对集体行为的干预效果?

How does the interaction radius affect the performance of intervention on collective behavior?

作者信息

Wang Caiyun, Han Jing

机构信息

LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

PLoS One. 2018 Feb 15;13(2):e0192738. doi: 10.1371/journal.pone.0192738. eCollection 2018.

DOI:10.1371/journal.pone.0192738
PMID:29447221
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5813976/
Abstract

The interaction radius r plays an important role in the collective behavior of many multi-agent systems because it defines the interaction network among agents. For the topic of intervention on collective behavior of multi-agent systems, does r also affect the intervention performance? In this paper we study whether it is easier to change the convergent heading of the group by adding some special agents (called shills) into the Vicsek model when r is larger (or smaller). Two kinds of shills are considered: fixed-heading shills (like leaders that never change their headings) and evolvable-heading shills (like normal agents but with carefully designed initial headings). We know that with the increase of r, two contradictory effects exist simultaneously: the influential area of a single shill is enlarged, but its influence strength is weakened. Which factor dominates? Through simulations and theoretical analysis we surprisingly find that r affects the intervention performance differently in different cases: when fixed-heading shills are placed together at the center of the group, larger r gives a better intervention performance; when evolvable-heading shills are placed together at the center, smaller r is better; when shills (either fixed-heading or evolvable-heading) are distributed evenly inside the group, the effect of r on the intervention performance is not significant. We believe these results will inspire the design of intervention strategies for many other multi-agent systems.

摘要

交互半径r在许多多智能体系统的集体行为中起着重要作用,因为它定义了智能体之间的交互网络。对于多智能体系统集体行为的干预这一主题,r是否也会影响干预性能呢?在本文中,我们研究当r较大(或较小)时,通过向Vicsek模型中添加一些特殊智能体(称为虚假智能体)来改变群体的收敛方向是否更容易。我们考虑了两种虚假智能体:固定方向虚假智能体(类似于从不改变方向的领导者)和可进化方向虚假智能体(类似于普通智能体,但具有精心设计的初始方向)。我们知道,随着r的增加,两种相互矛盾的效应同时存在:单个虚假智能体的影响区域扩大,但其影响强度减弱。哪个因素占主导呢?通过模拟和理论分析,我们惊人地发现,r在不同情况下对干预性能的影响不同:当固定方向虚假智能体放置在群体中心时,r越大,干预性能越好;当可进化方向虚假智能体放置在群体中心时,r越小越好;当虚假智能体(固定方向或可进化方向)均匀分布在群体内部时,r对干预性能的影响不显著。我们相信这些结果将为许多其他多智能体系统的干预策略设计提供启发。

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