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NARRATOR:Listen to part of a lecture in a biology class.
FEMALE PROFESSOR:I'd like to continue our discussion of animal behavior and start off today's class by focusing on a concept we haven't yet touched upon—swarm intelligence.
Swarm intelligence is a collective behavior that emerges from a group of animals, like a colony of termites, a school of fish, or a flock of birds.Let's first consider the principles behind swarm intelligence, and we'll use the ant as our model.Now, an ant on its own is not that smart. When you have a group of ants, however, there you have efficiency in action.You see, there's no leader running an ant colony.
Each individual, each individual ant operates by instinctively following a simple set of rules when foraging for food.Rule number 1: Deposit a chemical marker... called a pheromone. And rule 2: Follow the strongest pheromone path.The strongest pheromone path is advantageous to ants seeking food.So, for example, when ants leave the nest, they deposit a pheromone trail along the route they take.If they find food, they return to the nest on the same path and the pheromone trail gets stronger—it's doubled in strength.Because an ant that took a shorter path returns first, its pheromone trail is stronger, and other ants will follow it, according to rule 2.And as more ants travel that path, the pheromone trail gets even stronger.
So, what's happening here?Each ant follows two very basic rules, and each ant acts on information it finds in its immediate local environment.And it's important to note: Even though none of the individual ants is aware of the bigger plan, they collectively choose the shortest path between the nest and a food source because it's the most reinforced path.
By the way, a-a few of you have asked me about the relevance of what we're studying to everyday life.And swarm intelligence offers several good examples of how concepts in biology can be applied to other fields.Well, businesses have been able to use this approach of following simple rules when designing complex systems, for instance, in telephone networks.When a call is placed from one city to another, it has to connect through a number of nodes along the way.At each point, a decision has to be made: Which direction does the call go from here?Well, a computer program was developed to answer this question based on rules that are similar to the ones that ants use to find food.Remember, individual ants deposit pheromones, and they follow the path that is most reinforced.Now, in the phone network, a computer monitors the connection speed of each path, and identifies the paths that are currently the fastest—the least crowded parts of the network.And this information, converted into a numeric code, is deposited at the network nodes.This reinforces the paths that are least crowded at the moment.The rule the telephone network follows is to always select the path that is most reinforced.So, similar to the ant's behavior, at each intermediate node, the call follows the path that is most reinforced.This leads to an outcome which is beneficial to the network as a whole, and calls get through faster.
But getting back to animal behavior, another example of swarm intelligence is the way flocks of birds are able to fly together so cohesively.How do they coordinate their movements and know where they're supposed to be?Well, it basically boils down to three rules that each bird seems to follow.Rule 1: Stay close to nearby birds. Rule 2: Avoid collision with nearby birds. And rule 3: Move in the average speed and direction of nearby birds.
Oh, and by the way, if you're wondering how this approach can be of practical use for humans: The movie industry had been trying to create computer-generated flocks of birds in movie scenes.The question was how to do it easily on a large scale?A researcher used these three rules in a computer graphics program, and it worked!
There have also been attempts to create computer-generated crowds of people using this bird flocking model of swarm intelligence.However, I'm not surprised that more research is needed.The three rules I mentioned might be great for bird simulations, but they don't take into account the complexity and unpredictability of human behavior.So, if you want to create crowds of people in a realistic way, that computer model might be too limited.
旁白：请听生物学课上的部分内容。
教授：我想继续讨论动物行为，并通过关注一个我们还没触及的概念开始今天的课程，那就是群体智能。
群体智能是一种出现在一群动物中的集体行为，比如一群白蚁，一群鱼或一群鸟。我们首先来看看群体智能背后的一些原则，我们就用蚂蚁作为范例吧。单独的一只蚂蚁并没有那么聪明，但是有了一群蚂蚁就有了高效的行动。其实并没有领导者管理蚁群。
每只蚂蚁在搜寻食物时，通过下意识地遵从一套简单的规则来进行作业。规则一：留下一种叫做费洛蒙的化学标志。规则二：沿着费洛蒙最强烈的那条路走。对寻找食物的蚂蚁来说，费洛蒙最强烈的那条路非常有利。举个例子说，当蚂蚁离开巢穴时，它们会在自己走着的路线上留下一条费洛蒙痕迹。如果它们找到了食物，它们回蚁巢时会走同一条路，所以这条路上的费洛蒙会变得更强烈，因为强度翻番了。因为选择了短一些的路的蚂蚁会先回到蚁巢，它的费洛蒙痕迹更强烈，而根据第二条规则，其他的蚂蚁也会走这条路。随着越来越多的蚂蚁在这条路上走过，那里的费洛蒙痕迹就会变得更强。
那么这里会发生什么呢每只蚂蚁都会遵从这两条非常基本的规则，而且每只蚂蚁会根据它在当时的环境中发现的信息行动。有一个很重要的事情要注意，虽然没有任何一只蚂蚁意识到了更大的计划，但它们集体选择了蚁巢和食物之间最短的路，因为这是痕迹最强的路。
顺便提一下，你们中有些人问过我，我们正在学习的内容和日常生活有什么联系。群体智能提供了好几个很好的例子，说明了生物学中的概念如何能被用于其他领域。企业在设计复杂的系统时，可以使用这种遵从简单规则的方法，比如，在电话网中。从一座城市打电话到另一座城市时，它在这个过程中要通过很多节点进行连接。在每个节点，他们必须决定这通电话从这里要拨往哪个方向。他们按照和蚂蚁用来找到食物的相似规则，设计出了一个电脑程序来回答这个问题。别忘了，单个蚂蚁会留下费洛蒙，并且会沿着费洛蒙最强劲的那条路走。在电话网络中，电脑会监控每条路径的连接速度，并辨认出这个网络中目前速度最快，最不拥挤的部分。而这个转化成了数字代码的信息被留在了网络节点上。这加强了那时最不拥挤的线路。电话网络遵从的规定就是，总是选择那条最强劲的线路。所以，和蚂蚁的行为相似的，在每个中间节点上，这通电话会选择最强劲的那条线路。这带来了一个有益于整个网络的结果，而且电话能更快接通。
但是回到动物行为，群体智能的另一个例子就是鸟群能够在一起飞得特别团结。它们是如何协调自己的动作，并且知道自己应该在哪个位置的呢？归根结底基本上就是每只鸟似乎都会遵从三条规则。规则一：紧靠临近的鸟；规则二：避免撞到临近的鸟；规则三：按照临近鸟的平均速度和方向移动。
顺便说一下，如果你们想知道这个方法如何能为人类实际使用的话，电影行业就一直试图在电影画面中创造电脑生成的鸟群。问题是，如何在很大程度上容易做到这一点？一个研究人员在一个电脑制图程序中使用了这三条规则，结果是有效的。
还有人尝试用这种鸟群的群体智能创作出电脑生成的人群。但是，丝毫不令我意外的是，这还需要进行更多的研究。我提到的这三条规则对模拟鸟类可能很有效，但是它们没有考虑到人类行为的复杂性和不可预测性。所以，如果你想逼真地创作出人群的话，那个电脑模型也许太过有限。