OFFICIAL46 What are two principles of swarm intelligence based on the ant example? [Click on 2 answers.]

[00:00.00]NARRATOR: Listen to part of a lecture in a biology class.
[00:03.66]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.[00:12.74]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. [00:22.33]Let's first consider the principles behind swarm intelligence, and we'll use the ant as our model.
[00:28.24]Now, an ant on its own is not that smart. [00:31.62]When you have a group of ants, however, there you have efficiency in action. [00:35.66]You see, there's no leader running an ant colony. [00:38.49]Each individual, each individual ant operates by instinctively following a simple set of rules when foraging for food.
[00:46.50] Rule number 1: Deposit a chemical marker … called a pheromone. [00:50.66]And rule 2: Follow the strongest pheromone path. [00:53.99]The strongest pheromone path is advantageous to ants seeking food.
[00:58.34]So, for example, when ants leave the nest, they deposit a pheromone trail along the route they take. [01:04.42] If they find food, they return to the nest on the same path and the pheromone trail gets stronger—[01:10.18]it's doubled in strength.[01:11.68]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. [01:20.00]And as more ants travel that path, the pheromone trail gets even stronger.
[01:24.61]So, what's happening here? [01:26.40]Each ant follows two very basic rules, [01:29.07]and each ant acts on information it finds in its immediate local environment. [01:34.62]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.
[01:47.40]By the way, a-a few of you have asked me about the relevance of what we're studying to everyday life. [01:52.91]And swarm intelligence offers several good examples of how concepts in biology can be applied to other fields.
[01:58.72]Well, businesses have been able to use this approach of following simple rules when designing complex systems, [02:05.70]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. [02:14.34]At each point, a decision has to be made: Which direction does the call go from here? [02:19.11]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. [02:26.66]Remember, individual ants deposit pheromones, and they follow the path that is most reinforced.
[02:32.38]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. [02:42.71]And this information, converted into a numeric code, is deposited at the network nodes. [02:48.14]This reinforces the paths that are least crowded at the moment. [02:52.84]The rule the telephone network follows is to always select the path that is most reinforced. [02:58.63]So, similar to the ant's behavior, at each intermediate node, the call follows the path that is most reinforced. [03:05.80]This leads to an outcome which is beneficial to the network as a whole, and calls get through faster.[03:11.62]But getting back to animal behavior, another example of swarm intelligence is the way flocks of birds are able to fly together so cohesively. [03:19.66]How do they coordinate their movements and know where they're supposed to be?
[03:23.58]Well, it basically boils down to three rules that each bird seems to follow. [03:28.67]Rule 1: Stay close to nearby birds. [03:31.75]Rule 2: Avoid collision with nearby birds. [03:35.87]And rule 3: Move in the average speed and direction of nearby birds.
[03:41.57]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.
[03:52.04]The question was how to do it easily on a large scale? [03:56.08]A researcher used these three rules in a computer graphics program, and it worked![04:01.70]There have also been attempts to create computer-generated crowds of people using this bird flocking model of swarm intelligence.
[04:08.69]However, I'm not surprised that more research is needed. [04:12.79]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. [04:20.48]So, if you want to create crowds of people in a realistic way, that computer model might be too limited.