Smart swarms of bacteria-inspired agents with performance adaptable interactions.
Smart swarms of bacteria-inspired agents with performance adaptable interactions.
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DOI：
10.1371/journal.pcbi.1002177
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发表时间：
2011-09
影响因子：
4.3
通讯作者：
Ben-Jacob E
中科院分区：
文献类型：
--
作者：
Shklarsh A;Ariel G;Schneidman E;Ben-Jacob E
集体导航和群集已经在动物群体中进行了研究，如鱼群，鸟群，细菌和黏菌。计算机模拟表明，简单代理的集体行为可以由代理之间的简单相互作用引起，包括短程排斥，中程对齐和长程吸引。在这里，我们研究细菌启发的智能代理在复杂的地形集体导航，与自适应的相互作用，取决于性能。更具体地说，每个代理根据其本地环境调整其与其他代理的交互-通过减少对等体的影响力，同时朝着有利的方向导航，否则增加它。我们表明，包括这种性能依赖的自适应相互作用显着提高了集体群集性能，导致高效的导航，特别是在复杂的地形。值得注意的是，为了提供这种适应性强的交互，每个模型化的代理只需要具有短期记忆的简单计算能力，这可以很容易地在简单的群集机器人中实现。许多生物群体，从细菌和群居昆虫的群落，到鱼群和鸟群，再到哺乳动物群，都表现出先进的集体导航能力。识别受生物启发的交互代理的最小特征，这些特征可以导致“智能”的出现，如集体导航和决策，这是我们理解集体行为的基础，并且对人工智能和机器人技术非常感兴趣。以前的代理集体行为模型，依赖于排斥，定向（对齐）和吸引力的静态相互作用，已经显示了集体群集的出现。在这里，我们展示了在复杂地形中进行群体导航的性能自适应交互的优势。每个代理感知本地环境，然后允许根据其本地环境调整其与其他代理的交互-通过减少对等体的影响，同时朝着有利的方向导航，反之亦然。我们发现，包括这种适应性强的相互作用显着提高了集体群集性能，导致高效的导航，特别是在非常复杂的地形。
Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment – by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots. Many groups of organisms, from colonies of bacteria and social insects through schools of fish and flocks of birds to herds of mammals exhibit advanced collective navigation. Identifying the minimal features of biologically-inspired interacting agents that can lead to emergence of “intelligent” like collective navigation and decision making is fundamental to our understanding of collective behavior, and is of great interest in artificial intelligence and robotics. Previous models of collective behavior of agents, which relied on static interactions of repulsion, orientation (alignment), and attraction, have shown the emergence of collective swarming. Here we show the advantage of performance adaptable interactions for navigation of groups in complex terrains. Each agent senses the local environment and is then allowed to adjust its interactions with the other agents according to its local environment – by decreasing the peers' influence while navigating in a beneficial direction and vice versa. We found that inclusion of such adaptable interactions dramatically improves the collective swarming performance leading to highly efficient navigation especially in very complex terrains.
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DOI：
10.1098/rsta.2003.1199
发表时间：
2003-06-13
影响因子：
5
作者：
Ben-Jacob, E
通讯作者：
Ben-Jacob, E
DOI：
10.1073/pnas.0711437105
发表时间：
2008-01-29
影响因子：
11.1
作者：
Ballerini, M.;Calbibbo, N.;Zdravkovic, V.
通讯作者：
Zdravkovic, V.
影响因子：
2
作者：
BECKERS, R;DENEUBOURG, JL;GOSS, S
通讯作者：
GOSS, S
影响因子：
56.9
作者：
ADLER, J
通讯作者：
ADLER, J
影响因子：
64.8
作者：
BENJACOB, E;SCHOCHET, O;VICSEK, T
通讯作者：
VICSEK, T