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.

從網(wǎng)絡(luò)搜索引擎到軍用機器人，研究集群讓人類在?管理與運用復(fù)雜系統(tǒng)上又更進一步。

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.

簡單的個體動作如何組成復(fù)雜的群體行為？如果上百只蜜蜂無法達成一致，它們怎樣明智地決定蜂巢應(yīng)該建在哪里？為什么一群鯡魚能精準地相互協(xié)調(diào)，像一尾銀色的有機體來去自如？這些問題的答案與一個有趣的自然現(xiàn)象有關(guān)，我稱之為：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.

智群是指通過組成集體來應(yīng)對不確定性、復(fù)雜性與變化因素、能對環(huán)境與同伴作出反應(yīng)并以此增強力量的一群個體。比如說鳥吧。華盛頓的白宮附近有一個小公園，我喜歡在那里看鴿群在車流與樹木上空盤旋。有時候，它們會在公園周圍的房檐、窗框上停下休息。如果有什么東西驚擾了它們，它們又會不約而同地集體起飛。

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.

這些鴿子沒有領(lǐng)頭者。不會有哪只鴿子指揮它們接下來做什么。事實上，它們僅僅是密切關(guān)注旁邊的鴿子在做什么，在空中急轉(zhuǎn)彎的時候遵循簡單的規(guī)則。這些規(guī)則形成了另一種群體智慧——個體不做決定而是專注于精確的動作配合。

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.

計算機圖形學(xué)家Craig Reynolds很好奇這些背后的規(guī)則到底是什么。所以，他在1986年開發(fā)了看似簡單的導(dǎo)向程序boids。在此模擬過程中，每個設(shè)計成鳥類形狀的物體，也就是boids，都收到三個指令：1）與周邊的boids保持一定遠的距離以避免擠壓，2）以周邊boids的平均方向作為飛行方向，3）與周邊的boids保持一定近的距離以保持隊形。從電腦屏幕上的運行情況來看，這一試驗的最終動態(tài)，包括栩栩如生又不可人為預(yù)測的各種動作，正是對鳥群群集的成功模擬。

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.

當時，Reynolds在尋找在電視和電影中逼真描繪動物的方法。（1992年的《蝙蝠俠歸來(Batman Returns)》是第一部運用其想法的電影，影片中描繪了一群蝙蝠和一隊企鵝）如今他在索尼公司從事游戲研究，開發(fā)例如能夠?qū)崟r模擬多達15000只相互交流的鳥、魚或人所組成的群體的算法。

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向世人展現(xiàn)了自控模型在模擬群體行為方面的潛力，為?研究機器人技術(shù)的工程師們開辟了新的研究方向。相比單個機器人，能夠像鳥群一樣協(xié)作的機器人組顯然具有巨大優(yōu)勢。它們能分散并覆蓋大面積區(qū)域，形成強大的移動傳感網(wǎng)絡(luò)，收集外界的信息。若遇到突發(fā)情況，集群能夠快速調(diào)整應(yīng)對，哪怕是相對簡易的機器人組，也能像螞蟻一樣通過不斷試驗找到解決之道。要是集群中的一個個體掉鏈子，其它成員能夠及時填補空缺。最重要的是，整個集群的控制權(quán)是分散的，不必集中于領(lǐng)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."

“生物學(xué)中，你會發(fā)現(xiàn)大數(shù)量群體中有中心領(lǐng)導(dǎo)者的其實不多，”賓夕法尼亞大學(xué)機械工程學(xué)教授Vijay Kumar說，“一切都是分散的：它們不會跟彼此講話。它們根據(jù)周邊數(shù)據(jù)行動。個體特征被忽略。我不管是誰做的，只要有人做就行。從單個機器人推廣到多個機器人，這三點都很重要?！?/p>

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希望在五年內(nèi)推出一個網(wǎng)絡(luò)化協(xié)作的機器人車組。其中一個主要目標是用作一線應(yīng)援?！氨热缯f現(xiàn)在有人打911呼救，”他說，“火警警報響了。你不想讓人去應(yīng)援，想讓機器去查看情況。在消防隊進入著火的樓之前，為什么不讓一組機器人先去呢？”

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.

布魯塞爾的Marco Dorigo團隊則更進一步。這群歐洲人嘗試開發(fā)名為“swarmanoid”的分工協(xié)作式機器人組：“foot-bots”負責(zé)地面物體的傳運，“hand-bots”能攀援墻面與操作物體，“eye-bots”可以飛來飛去，為其他部門提供數(shù)據(jù)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.

軍方對類似應(yīng)用求之若渴。2004年1月20日弗吉尼亞的弗雷德里克斯堡附近，研究人員在Fort A. P. Hill訓(xùn)練中心的空辦公樓中測試由66個小型機器人組成的機器人集群。它們的任務(wù)是：在大樓中找到隱藏的目標。

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.")

這些30厘米長的紅色機器人在主廊道上迅速移動，靠三個輪子靈巧轉(zhuǎn)向，酷似巨大的昆蟲。每個?部件上都裝有八個雷達，以防與墻和其它機器人發(fā)生碰撞。它們分頭行動逐個搜查房間，用網(wǎng)狀小鏡頭尋找可疑物品。兩個機器人相遇之后會通過無線網(wǎng)絡(luò)裝置?交流情報。（“嘿，那邊我剛瞧過了，去別處看看。”）

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.

在其中一個房間后方，一個機器人發(fā)現(xiàn)了可疑情況：在打開的衣柜里有一個粉紅色的球（它們的目標是尋找一切粉紅色的東西）。這個機器人呆住，給人類管理者發(fā)了張照片。不久，一些機器人也來到這間房子，在粉紅色入侵者的周圍圍了個圓。半個小時之內(nèi)，藏匿的六個目標全部被找到。開展實驗的研究團隊宣布實驗成功。之后他們又開始了新的嘗試。

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.

這次實踐是Centibots項目的一部分，研究多達一百個機器人能否合作完成一個任務(wù)。如果能，機器人組將來可能會被派往敵方村落排查恐怖分子、確定囚犯位置，到受震房屋里尋找傷者，到化學(xué)物質(zhì)泄漏區(qū)調(diào)查有毒有害物排放，又或者在邊境偵查入侵者。軍事機構(gòu)，比如說DARPA（美國國防高級研究計劃局），資助了包括合作型智群理念下的直升機組、固定翼機組、魚雷狀水下滑翔機組、無人地面車組在內(nèi)的一系列機器人研究項目。但在當時，這已經(jīng)是試驗過的最大型集群了。

"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."

“當我們開始做Centibots項目的時候，我們都覺得：這太瘋狂了，根本不可能做到，”加州門羅公園的斯坦福國際咨詢研究所研究人員Régis Vincent說，“而現(xiàn)在我們希望試試我們能不能做到一千個機器人?！?/p>

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

當然自然界中的動物們甚至以更大數(shù)量的群體遷移。

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.

這是因為無論是鳥群、魚群還是羊群，大集體能夠更好地發(fā)現(xiàn)天敵、食物、伴侶，也更容易跟隨遷移的路線。對這些動物來說，彼此間動作協(xié)調(diào)事關(guān)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."

“一千只魚比一只魚更容易發(fā)現(xiàn)捕食者，”華盛頓大學(xué)生物學(xué)家Daniel Grünbaum說，“捕食者正在靠近的消息能在魚群中迅速傳開，因為魚可以從身邊的同伴那里感受到危險信號?！?/p>

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.

捕食者攻擊魚群的時候，魚群能迅速分散以至于捕食者幾乎不可能追到任何一只。魚群就像是瞬間爆炸，在捕食者周圍制造移動氣泡，或者分裂成無數(shù)小點，然后重新集聚再游走。這就是群體智慧的非凡魅力。不管是螞蟻、蜜蜂、鴿子還是馴鹿，智群行為的要素——分散化控制模式、關(guān)注周邊信息的反應(yīng)模式和簡單的經(jīng)驗規(guī)則——組成了能夠應(yīng)對復(fù)雜變化的機智策略。

"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."

“我們甚至不知道還能怎么做，”馬薩諸塞劍橋Icosystem公司復(fù)雜理論學(xué)家、首席科學(xué)家Eric Bonabeau說，“我們還不適應(yīng)用分散化的方法解決分散化問題。我們不能用到處放停止標志牌和交通信號燈的方法來管理交通這樣的新興現(xiàn)象。但把交通變成自我調(diào)節(jié)系統(tǒng)的想法，確實非常有意思?！?/p>

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.

有的社會、政治團體已經(jīng)采用了智群策略，盡管尚不成熟。八年前西雅圖群眾抗議活動中，反全球化活動者用移動通訊設(shè)備快速發(fā)布警察動態(tài)信息，把原本無組織的群眾變成能夠像魚群一樣分散和重組的“智能團體”。

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.”

而最大的變化發(fā)生在互聯(lián)網(wǎng)上。試想Google是如何利用群體智能搜索你想找的內(nèi)容的。當你鍵入搜索指令后，Google在它的索引服務(wù)器上查閱數(shù)十億網(wǎng)頁來篩選出最有關(guān)聯(lián)的，然后將它們根據(jù)被鏈接數(shù)和權(quán)重（最熱門的網(wǎng)站相對重要，因為它們更有可能準確可靠）排序，最高票結(jié)果會排在搜索結(jié)果之首。由此，Google說它“利用網(wǎng)絡(luò)的集體智慧來決定網(wǎng)頁的重要程度”。

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."

事實證明維基百科獲得了巨大的成功，這個自由的協(xié)作式百科全書擁有超過兩百種語言寫就、內(nèi)容涵蓋天下所有事物的數(shù)百萬個詞條，每個人都能貢獻和修改?！叭缃翊罅坑脩粢黄饏f(xié)作變成可能，這是在幾十年前不可想象的狀況?！甭槭±砉W(xué)院新的集群智能中心的Thomas Malone說，“沒有哪個人能夠知道解決社會問題的所有相關(guān)知識，比如說醫(yī)療保障和氣候變化，但以綜合協(xié)作的方式，我們就能獲得比我們目前所知更多的知識?！?/p>

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.?

以上想法突顯出一個關(guān)于集群智能的重要事實：只有當個體成員表現(xiàn)得負責(zé)任并自主決定，群體才會有智慧。如果成員們只會互相模仿、盲目跟風(fēng)或者等待別人告訴自己應(yīng)該做什么，一個群體就不可能成為智群。不管是螞蟻還是馴鹿，智能的群體需要每位成員做好自己的角色。我們之中有些人可能有時會疑惑多回收一個塑料瓶在減輕人類對地球的作用上到底有多大用處，但最重要的是其實每一個動作都會產(chǎn)生影響，哪怕我們還不能理解它如何影響。

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.?

群體行動是牧群動物用以防避捕食者的重要武器。野生動物學(xué)家Karsten Heuer和他的妻子Leanne Allison曾在加拿大研究一大群馴鹿。他們發(fā)現(xiàn)鹿群會在有狼準備悄悄靠近的時候作出經(jīng)典的智群防御反應(yīng)。

"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.

“（離狼）最近的馴鹿會轉(zhuǎn)向逃跑，這一反應(yīng)像波浪在整個鹿群中傳播開來，最后所有的鹿都在逃跑?！盚euer說。每一只馴鹿都開始轉(zhuǎn)身逃跑，仿佛狼逼近的是自己。最后，鹿群從山脊上逃離了，留下那只狼氣喘吁吁地吞雪止渴。

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風(fēng)聲鶴唳草木皆兵，反而表現(xiàn)出高準確性。每只馴鹿都知道當時應(yīng)該逃跑、往哪個方向跑，即使它并不清楚為什么要跑。不需要專門的領(lǐng)頭鹿協(xié)調(diào)調(diào)動其他成員。事實上，數(shù)千年與狼的斗爭演化出一套簡單的智群規(guī)則，而每一頭鹿只需要遵守就行了。

（譯自Integrated Study課教材Pathways4）