Swarm intelligence inspired by animals

Enhancing photographs
Aladdin Ayesh, coordinator for the Intelligent Mobile Robots and Creative Computing Research Group at De Montfort University in the United Kingdom, said his team’s innovation relies instead on a simple swarm intelligence that treats every pixel of a digital photo like a member of a swirling mass of particles, with a specific speed and direction.

With each new position, every pixel essentially uses a set of rules to look at where it is, where it might go next, and whether a new direction will leave it better or worse off in relation to its neighbors. By repeating that basic decision-making process many times, the pixels organize themselves in a way that enhances a whole image by improving its contrast.

“The good thing about this type of algorithm is that the particle only has a velocity and a direction and you only have rules that tell the particle where to move and rules that tell it what is a good position or bad position,” Ayesh said. Again, the simplicity means the computation can be done with limited resources.

He and two collaborators at Jordan’s Al-Balqa Applied University found that, at best, their contrast-sharpening method can outperform other techniques. At worst, the strategy they published in a recent issue of the International Journal of Innovative Computing and Applications was roughly equivalent, but only required a fraction of the computational energy. Eventually, Ayesh said, it could be incorporated into a suite of photo-editing applications or called upon to search for items of interest within grainy surveillance videos, especially items that might otherwise blend in with the foreground or background.

For another project, Ayesh and a graduate student assembled a group of firefighting robots and sent them off in the direction of a blaze (in a simulator, anyway). As with the project by Cui and St. Charles, the robots were governed by flocking rules that kept them from venturing too close or too far away from each other while moving in the same general heading. Varying the rule definitions led to different group behaviors. Tweaking the group’s relative cohesion, for example, changed whether a lost or stuck robot effectively delayed the entire team.

Eventually, the researchers found that a group of about 50 robots worked best for a task that might, in real life, require the rapid deployment of a search-and-rescue or firefighting team that wouldn’t be undermined by the loss of a few units. Encouragingly, when the scientists imported their control and communication software into a handful of real robots, the system worked as expected. In addition, Ayesh said the research could help with the kind of computer vision advances necessary for firefighting robots to correctly identify fellow members while choosing where to aim a stream of water.

Influencing neighbors’ behavior
Tucker Balch, an associate professor in Interactive and Intelligent Computing at Georgia Institute of Technology in Atlanta, said flock-based applications such as finding similar documents in a database work by distributing little bits of intelligence across large areas. “The key insight there is really to convert the problem from a centralized compare-everything-to-everything problem into a decentralized approach,” Balch said. “They’re ascribing to each paper a little bit of autonomy.”

As with the method’s original inspirations, “the general idea is that each insect or animal can perceive and react to what it sees locally,” said Balch, who co-directs Georgia Tech’s “Borg Lab,” an interdisciplinary effort named in honor of the collective-minded villains of “Star Trek” fame.

No individual fully grasps the entire problem, whether the task is to defend a hive or migrate in a coordinated fashion, he said. But by acting on its own senses within its immediate surroundings, each animal influences its neighbors’ behavior. Magnified across a large flock or swarm, “you get a global behavior of the whole system that can appear intelligent.”

In other words, the sum is greater than its parts.

The end result isn’t guaranteed to be the best solution, he cautioned. “The idea is that you get a fairly good solution fairly quickly, as opposed to no solution or maybe the optimal solution but it takes a long, long time.” Even so, for many applications, he said, “you need a fast solution, not necessarily the optimal solution.”

Unlike the famous “I Love Lucy” episode when Lucy and Ethel’s assembly line job wrapping chocolates ends in a comical disaster, the collective intelligence of wasps has inspired other researchers to develop a formulation that efficiently assigns cars to different painting stations as they continuously roll off the assembly line.

And like Ayesh’s bird-inspired simulation of firefighting robots, Balch said animal-based solutions lack a central point of failure — a fatal weakness of the droid-controlling ship on “Star Wars Episode I: The Phantom Menace,” which is finally torpedoed by Anakin Skywalker, the movie’s young hero. The result? An entire droid army falls over.

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