The Smart Swarm

by Peter Miller 

The study of swarms is providing insights that can help humans manage complex systems, from online search engines to military robots.

从网络搜索引擎到军用机器人，研究集群让人类在�管理与运用复杂系统上又更进一步。

How do the simple actions of individuals add up to the complex behaviour of a group? How do hundreds of honeybees make a critical decision about their hive if many of them disagree? What enables a school of herring to coordinate its movements so precisely it can change direction in a flash, like a single, silvery organism? The answer has to do with a remarkable phenomenon I call smart swarm.

简单的个体动作如何组成复杂的群体行为？如果上百只蜜蜂无法达成一致，它们怎样明智地决定蜂巢应该建在哪里？为什么一群鲱鱼能精准地相互协调，像一尾银色的有机体来去自如？这些问题的答案与一个有趣的自然现象有关，我称之为：智群。

A smart swarm is a group of individuals who respond to one another and to their environment in ways that give them power, as a group to cope with uncertainty, complexity, and change. Take birds, for example. There's a small park near the White House in Washington, D.C., where I like to watch flocks of pigeons swirl over the traffic and trees. Sooner or later, the birds come to rest on ledges of buildings surrounding the park. Then something disrupts them, and they're off again in synchronised flight.

智群是指通过组成集体来应对不确定性、复杂性与变化因素、能对环境与同伴作出反应并以此增强力量的一群个体。比如说鸟吧。华盛顿的白宫附近有一个小公园，我喜欢在那里看鸽群在车流与树木上空盘旋。有时候，它们会在公园周围的房檐、窗框上停下休息。如果有什么东西惊扰了它们，它们又会不约而同地集体起飞。

The birds don't have a leader. No pigeon is telling the others what to do. Instead, they're each paying close attention to the pigeons next to them, each bird following simple rules as they wheel across the sky. These rules add up to another kind of swarm intelligence—one that has less to do with making decisions than with precisely coordinating movement.

这些鸽子没有领头者。不会有哪只鸽子指挥它们接下来做什么。事实上，它们仅仅是密切关注旁边的鸽子在做什么，在空中急转弯的时候遵循简单的规则。这些规则形成了另一种群体智慧——个体不做决定而是专注于精确的动作配合。

Craig Reynolds, a computer graphics researcher, was curious about what these rules might be. So, in 1986, he created a deceptively simple steering program called boids. In this simulation, generic birdlike objects, or boids, were each given three instructions: 1) avoid crowding nearby boids, 2) fly in the average direction of nearby boids, and 3) stay close to nearby boids. The result, when set in motion on a computer screen, was a convincing simulation of flocking, including lifelike and unpredictable movements.

计算机图形学家Craig Reynolds很好奇这些背后的规则到底是什么。所以，他在1986年开发了看似简单的导向程序boids。在此模拟过程中，每个设计成鸟类形状的物体，也就是boids，都收到三个指令：1）与周边的boids保持一定远的距离以避免挤压，2）以周边boids的平均方向作为飞行方向，3）与周边的boids保持一定近的距离以保持队形。从电脑屏幕上的运行情况来看，这一试验的最终动态，包括栩栩如生又不可人为预测的各种动作，正是对鸟群群集的成功模拟。

At the time, Reynolds was looking for ways to depict animals realistically in TV shows and films. (Batman Returns in 1992 was the first movie to use his approach, portraying a swarm of bats and an army of penguins.) Today he works at Sony doing research for games, such as an algorithm that simulates in real time as many as 15,000 interacting birds, fish, or people.

当时，Reynolds在寻找在电视和电影中逼真描绘动物的方法。（1992年的《蝙蝠侠归来(Batman Returns)》是第一部运用其想法的电影，影片中描绘了一群蝙蝠和一队企鹅）如今他在索尼公司从事游戏研究，开发例如能够实时模拟多达15000只相互交流的鸟、鱼或人所组成的群体的算法。

By demonstrating the power of self-organizing models to mimic swarm behaviour, Reynolds was also blazing the trail for robotics engineers. A team of robots that could coordinate its actions like a flock of birds could offer significant advantages over a solitary robot. Spread out over a large area, a group could function as a powerful mobile sensor net, gathering information about what's out there. If the group encountered something unexpected, it could adjust and respond quickly, even if the robots in the group weren't very sophisticated, just as ants are able to come up with various options by trial and error. If one member of the group were to break down, others could take its place. And, most important, control of the group could be decentralized, not dependent on a leader.

Reynolds向世人展现了自控模型在模拟群体行为方面的潜力，为�研究机器人技术的工程师们开辟了新的研究方向。相比单个机器人，能够像鸟群一样协作的机器人组显然具有巨大优势。它们能分散并覆盖大面积区域，形成强大的移动传感网络，收集外界的信息。若遇到突发情况，集群能够快速调整应对，哪怕是相对简易的机器人组，也能像蚂蚁一样通过不断试验找到解决之道。要是集群中的一个个体掉链子，其它成员能够及时填补空缺。最重要的是，整个集群的控制权是分散的，不必集中于领袖。

"In biology, if you look at groups with large numbers, there are very few examples where you have a central agent," says Vijay Kumar, a professor of mechanical engineering at the University of Pennsylvania. "Everything is very distributed: They don't all talk to each other. They act on local information. And they're all anonymous. I don't care who moves the chair, as long as somebody moves the chair. To go from one robot to multiple robots, you need all three of those ideas."

“生物学中，你会发现大数量群体中有中心领导者的其实不多，”宾夕法尼亚大学机械工程学教授Vijay Kumar说，“一切都是分散的：它们不会跟彼此讲话。它们根据周边数据行动。个体特征被忽略。我不管是谁做的，只要有人做就行。从单个机器人推广到多个机器人，这三点都很重要。”

Within five years Kumar hopes to put a networked team of robotic vehicles in the field. One purpose might be as first responders. "Let's say there's a 911 call," he says. "The fire alarm goes off. You don't want humans to respond. You want machines to respond, to tell you what's happening. Before you send firemen into a burning building, why not send in a group of robots?"

Kumar希望在五年内推出一个网络化协作的机器人车组。其中一个主要目标是用作一线应援。“比如说现在有人打911呼救，”他说，“火警警报响了。你不想让人去应援，想让机器去查看情况。在消防队进入着火的楼之前，为什么不让一组机器人先去呢？”

Taking this idea one step further, Marco Dorigo's group in Brussels is leading a European effort to create a "swarmanoid," a group of cooperating robots with complementary abilities: "foot-bots" to transport things on the ground, "hand-bots" to climb walls and manipulate objects, and "eye-bots" to fly around, providing information to the other units.

布鲁塞尔的Marco Dorigo团队则更进一步。这群欧洲人尝试开发名为“swarmanoid”的分工协作式机器人组：“foot-bots”负责地面物体的传运，“hand-bots”能攀援墙面与操作物体，“eye-bots”可以飞来飞去，为其他部门提供数据信息。

The military is eager to acquire similar capabilities. On January 20, 2004, researchers released a swarm of 66 pint-size robots into an empty office building at Fort A. P. Hill, a training centre near Fredericksburg, Virginia. The mission: Find targets hidden in the building.

军方对类似应用求之若渴。2004年1月20日弗吉尼亚的弗雷德里克斯堡附近，研究人员在Fort A. P. Hill训练中心的空办公楼中测试由66个小型机器人组成的机器人集群。它们的任务是：在大楼中找到隐藏的目标。

Zipping down the main hallway, the foot-long (30 cm) red robots pivoted this way and that on their three wheels, resembling nothing so much as large insects. Eight sonars on each unit helped them avoid collisions with walls and other robots. As they spread out, entering one room after another, each robot searched for objects of interest with a small, Web-style camera. When one robot encountered another, it used wireless network gear to exchange information. ("Hey, I've already explored that part of the building. Look somewhere else.")

这些30厘米长的红色机器人在主廊道上迅速移动，靠三个轮子灵巧转向，酷似巨大的昆虫。每个�部件上都装有八个雷达，以防与墙和其它机器人发生碰撞。它们分头行动逐个搜查房间，用网状小镜头寻找可疑物品。两个机器人相遇之后会通过无线网络装置�交流情报。（“嘿，那边我刚瞧过了，去别处看看。”）

In the back of one room, a robot spotted something suspicious: a pink ball in an open closet (the swarm had been trained to look for anything pink). The robot froze, sending an image to its human supervisor. Soon several more robots arrived to form a perimeter around the pink intruder. Within half an hour, all six of the hidden objects had been found. The research team conducting the experiment declared the run a success. Then they started a new test.

在其中一个房间后方，一个机器人发现了可疑情况：在打开的衣柜里有一个粉红色的球（它们的目标是寻找一切粉红色的东西）。这个机器人呆住，给人类管理者发了张照片。不久，一些机器人也来到这间房子，在粉红色入侵者的周围围了个圆。半个小时之内，藏匿的六个目标全部被找到。开展实验的研究团队宣布实验成功。之后他们又开始了新的尝试。

The demonstration was part of the Centibots project, an investigation to see if as many as a hundred robots could collaborate on a mission. If they could, teams of robots might someday be sent into a hostile village to flush out terrorists or locate prisoners; into an earthquake-damaged building to find victims; onto chemical-spill sites to examine hazardous waste; or along borders to watch for intruders. Military agencies such as DARPA (Defence Advanced Research Projects Agency) have funded a number of robotics programs using collaborative flocks of helicopters and fixed-wing aircraft, schools of torpedo-shaped underwater gliders, and herds of unmanned ground vehicles. But at the time, this was the largest swarm of robots ever tested.

这次实践是Centibots项目的一部分，研究多达一百个机器人能否合作完成一个任务。如果能，机器人组将来可能会被派往敌方村落排查恐怖分子、确定囚犯位置，到受震房屋里寻找伤者，到化学物质泄漏区调查有毒有害物排放，又或者在边境侦查入侵者。军事机构，比如说DARPA（美国国防高级研究计划局），资助了包括合作型智群理念下的直升机组、固定翼机组、鱼雷状水下滑翔机组、无人地面车组在内的一系列机器人研究项目。但在当时，这已经是试验过的最大型集群了。

"When we started Centibots, we were all thinking, this is a crazy idea, it's impossible to do," says Régis Vincent, a researcher at SRI International in Menlo Park, California. “Now we're looking to see if we can do it with a thousand robots."

“当我们开始做Centibots项目的时候，我们都觉得：这太疯狂了，根本不可能做到，”加州门罗公园的斯坦福国际咨询研究所研究人员Régis Vincent说，“而现在我们希望试试我们能不能做到一千个机器人。”

In nature, of course, animals travel in even larger numbers.

当然自然界中的动物们甚至以更大数量的群体迁移。

That's because, as members of a big group, whether it's a flock, school, or herd, individuals increase their chances of detecting predators, finding food, locating a mate, or following a migration route. For these animals, coordinating their movements with one another can be a matter of life or death.

这是因为无论是鸟群、鱼群还是羊群，大集体能够更好地发现天敌、食物、伴侣，也更容易跟随迁移的路线。对这些动物来说，彼此间动作协调事关生死。

"It's much harder for a predator to avoid being spotted by a thousand fish than it is to avoid being spotted by one," says Daniel Grünbaum, a biologist at the University of Washington. “News that a predator is approaching spreads quickly through a school because fish sense from their neighbours that something's going on."

“一千只鱼比一只鱼更容易发现捕食者，”华盛顿大学生物学家Daniel Grünbaum说，“捕食者正在靠近的消息能在鱼群中迅速传开，因为鱼可以从身边的同伴那里感受到危险信号。”

When a predator strikes a school of fish, the group is capable of scattering in patterns that make it almost impossible to track any individual. It might explode in a flash, create a kind of moving bubble around the predator, or fracture into multiple blobs, before coming back together and swimming away. That's the wonderful appeal of swarm intelligence. Whether we’re talking about ants, bees, pigeons, or caribou, the ingredients of smart group behavior—decentralised control, response to local cues, simple rules of thumb—add up to a shrewd strategy to cope with complexity.

捕食者攻击鱼群的时候，鱼群能迅速分散以至于捕食者几乎不可能追到任何一只。鱼群就像是瞬间爆炸，在捕食者周围制造移动气泡，或者分裂成无数小点，然后重新集聚再游走。这就是群体智慧的非凡魅力。不管是蚂蚁、蜜蜂、鸽子还是驯鹿，智群行为的要素——分散化控制模式、关注周边信息的反应模式和简单的经验规则——组成了能够应对复杂变化的机智策略。

"We don't even know yet what else we can do with this," says Eric Bonabeau, a complexity theorist and the chief scientist at Icosystem Corporation in Cambridge, Massachusetts. "We're not used to solving decentralised problems in a decentralised way. We can't control an emergent phenomenon like traffic by putting stop signs and lights everywhere. But the idea of shaping traffic as a self-organizing system, that’s very exciting."

“我们甚至不知道还能怎么做，”马萨诸塞剑桥Icosystem公司复杂理论学家、首席科学家Eric Bonabeau说，“我们还不适应用分散化的方法解决分散化问题。我们不能用到处放停止标志牌和交通信号灯的方法来管理交通这样的新兴现象。但把交通变成自我调节系统的想法，确实非常有意思。”

Social and political groups have already adopted crude swarm tactics. During mass protests eight years ago in Seattle, anti-globalisation activists used mobile communications devices to spread news quickly about police movements, turning an otherwise unruly crowd into a “smart mob" that was able to disperse and re-form like a school of fish.

有的社会、政治团体已经采用了智群策略，尽管尚不成熟。八年前西雅图群众抗议活动中，反全球化活动者用移动通讯设备快速发布警察动态信息，把原本无组织的群众变成能够像鱼群一样分散和重组的“智能团体”。

The biggest changes may be on the Internet. Consider the way Google uses group smarts to find what you're looking for. When you type in a search query, Google surveys billions of Web pages on its index servers to identify the most relevant ones. It then ranks them by the number of pages that link to them, counting links as votes (the most popular sites get weighted votes, since they're more likely to be reliable). The pages that receive the most votes are listed first in the search results. In this way, Google says, it “uses the collective intelligence of the Web to determine a page's importance.”

而最大的变化发生在互联网上。试想Google是如何利用群体智能搜索你想找的内容的。当你键入搜索指令后，Google在它的索引服务器上查阅数十亿网页来筛选出最有关联的，然后将它们根据被链接数和权重（最热门的网站相对重要，因为它们更有可能准确可靠）排序，最高票结果会排在搜索结果之首。由此，Google说它“利用网络的集体智慧来决定网页的重要程度”。

Wikipedia, a free collaborative encyclopedia, has also proved to be a big success, with millions of articles in more than 200 languages about everything under the sun, each of which can be contributed by anyone or edited by anyone. "It's now possible for huge numbers of people to think together in ways we never imagined a few decades ago," says Thomas Malone of MIT's new Centre for Collective Intelligence. “No single person knows everything that's needed to deal with problems we face as a society, such as health care or climate change, but collectively we know far more than we've been able to tap so far."

事实证明维基百科获得了巨大的成功，这个自由的协作式百科全书拥有超过两百种语言写就、内容涵盖天下所有事物的数百万个词条，每个人都能贡献和修改。“如今大量用户一起协作变成可能，这是在几十年前不可想象的状况。”麻省理工学院新的集群智能中心的Thomas Malone说，“没有哪个人能够知道解决社会问题的所有相关知识，比如说医疗保障和气候变化，但以综合协作的方式，我们就能获得比我们目前所知更多的知识。”

Such thoughts underline an important truth about collective intelligence: Crowds tend to be wise only if individual members act responsibly and make their own decisions. A group won't be smart if its members imitate one another, slavishly follow fads, or wait for someone to tell them what to do. When a group is being intelligent, whether it's made up of ants or attorneys, it relies on its members to do their own part. For those of us who sometimes wonder if it’s really worth recycling that extra bottle to lighten our impact on the planet, the bottom line is that our actions matter, even if we don't see how. 

以上想法突显出一个关于集群智能的重要事实：只有当个体成员表现得负责任并自主决定，群体才会有智慧。如果成员们只会互相模仿、盲目跟风或者等待别人告诉自己应该做什么，一个群体就不可能成为智群。不管是蚂蚁还是驯鹿，智能的群体需要每位成员做好自己的角色。我们之中有些人可能有时会疑惑多回收一个塑料瓶在减轻人类对地球的作用上到底有多大用处，但最重要的是其实每一个动作都会产生影响，哪怕我们还不能理解它如何影响。

Wisdom of the Herd

牧群智能

Group behaviour can be vital for herd animals to avoid predators. Karsten Heuer, a wildlife biologist, and his wife, Leanne Allison, were studying a large caribou herd in Canada. When they spotted a wolf creeping toward the caribou, they noted that the herd responded with a classic swarm defence. 

群体行动是牧群动物用以防避捕食者的重要武器。野生动物学家Karsten Heuer和他的妻子Leanne Allison曾在加拿大研究一大群驯鹿。他们发现鹿群会在有狼准备悄悄靠近的时候作出经典的智群防御反应。

"The nearest caribou [to the wolf] turned and ran, and that response moved like a wave through the entire herd until they were all running.” Heuer said. Each animal turned and ran as the wolf approached it. In the end, the herd escaped over the ridge, and the wolf was left panting and gulping snow.

“（离狼）最近的驯鹿会转向逃跑，这一反应像波浪在整个鹿群中传播开来，最后所有的鹿都在逃跑。”Heuer说。每一只驯鹿都开始转身逃跑，仿佛狼逼近的是自己。最后，鹿群从山脊上逃离了，留下那只狼气喘吁吁地吞雪止渴。

The herd’s evasive manoeuvres displayed not panic, but precision. Every caribou knew when it was time to run and in which direction to go, even if it didn't know exactly why. No leader was responsible for coordinating the rest of the herd. Instead, each animal was following simple rules evolved over thousands of years of wolf attacks.

鹿群的逃避策略并不是风声鹤唳草木皆兵，反而表现出高准确性。每只驯鹿都知道当时应该逃跑、往哪个方向跑，即使它并不清楚为什么要跑。不需要专门的领头鹿协调调动其他成员。事实上，数千年与狼的斗争演化出一套简单的智群规则，而每一头鹿只需要遵守就行了。

（译自Integrated Study课教材Pathways4）