نطق اول

Lecture 1 - Swarm intelligence

Listen to part of a lecture in a biology class.

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 is 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 one: deposit a chemical marker called a pheromone. And rule two: 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 two. 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 the food source, because it’s the most reinforced path.

By the way, a few you have asked me about the relevance of what we are 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 one: stay close to nearby birds. Rule two: avoid collision with nearby birds. And rule three: 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’s been trying 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.