emergent behavior and other algorithms in nature

Nature has seemingly equipped army ants with just the right algorithm for on-the-go bridge building. Researchers working to build swarms of simple robots are still searching for the instructions that will allow their cheap machines to perform similar feats. One challenge they have to contend with is that nature makes ants more reliably, and at lower cost, than humans can fabricate swarm-bots, whose batteries tend to die. A second is that it’s very possible there’s more governing army ant behavior than two simple rules. Flocking, schooling, and herding are examples of the emergent behavior of a system resulting from self-organization. The enigma is that the system's emergent behavior can be more and different than that of any individual in the system.

1. bridges in Mid-air

Army ants exhibit complex social behaviors that require group cooperation to achieve success. These ants build bridges with their bodies. The lead ant will slow down when it reaches a gap. The rest of the colony will continue to build the bridge with their bodies until the gap is closed. Ants communicate using just 10 to 20 signals. Unlike human language, these messages are all instinctual. This example of cooperation goes to show why ants are such successful survivors.

2. emergence

Emergence is the phenomenon whereby larger entities arise through interactions among smaller/simpler entities such that the larger entities exhibit properties the smaller/simpler entities don't exhibit

2 of 5: what is emergence?

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3.  Emergent behavior

How does a group of animals or cells, for that matter work together when no one’s in charge? Tiny swarming robots called Kilobots, work together to tackle tasks in the lab, but what can they teach us about the natural world?

4. Flocking emergence

The only rule in the java code here is that each bird try to maintain a distance of 20 pixels from any other bird.

3 of 4: no one is incharge

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4 of 5: flocking algorithm

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5 of 5: artificial intelligence : understanding Logic behind " Cohesion "

  
         interactive demo of the flocking algorithm :

An implementation of Craig Reynold's Boids program to simulate the flocking behavior of birds. Each boid steers itself based on rules of avoidance, alignment, and coherence.  Click/touch below to add one new boid:

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Following are the descriptions of two different mechanisms that produce emergent behavior. The first describes behavior arising from groups of entities that have awareness of one another. The second describes emergent behavior arising from groups of entities that have no awareness of any others in the group. (The name "boid" corresponds to a shortened version of "bird-oid object", which refers to a bird-like object. Incidentally, "boid" is also a New York Metropolitan dialect pronunciation for "bird")

Flocking:  Flocking is a beautiful demonstration of emergent behavior. Amongst the programming community it’s also probably the best know. It was discovered by Craig Reynolds, a researcher presently working for Sony’s R&D dept. Craig was interested in the way birds flock together and discovered that you can obtain very similar behavior with virtual ‘Boids’ if they followed these three simple rules:

1. Separation:  Is a rule that steers a Boid away from those in its neighborhood region. When applied to a number of Boids they will spread out trying to maximize their distance from every other Boid. (In the same way that people spread out on a beach).

2. Alignment:  Attempts to keep a Boid’s heading aligned with its neighbors.

3. Cohesion:  Acts to keep a group of Boids together. A sheep running after its flock is demonstrating cohesive behavior. The figure above illustrates each behavior. The grey circle represents the white Boid’s ‘neighborhood region’. This is effectively it’s perceptive horizon, and it will only consider the Boids laying within this area when applying each rule.

Swarming and collective decision making is now observed as important to the immune system and bacterial infection. Biologically inspired solutions can now be applied to technological problems for example flocking and swarming algorithms can be used to
navigate autonomous cars.

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