Brainy Robots - Robotics

To do this, Ayanna and her colleagues rely on two concepts in the field of artificial intelligence: "fuzzy logic" and "neural networks."

Fuzzy logic allows computers to operate not only in terms of black and white -- true or false -- but also in shades of gray. For example, a traditional computer would take the height measurement of a tree and assign that tree to some category -- say, "tall." But a fuzzy logic computer would say the tree has a 78 percent chance (for example) of belonging to the category "tall" and a 22 percent chance of belonging to some other category. The sharp distinction between "tall" and "short" becomes fuzzy.

This probabilistic approach to categorisation allows the computer to learn from its experiences, since the assigning of probabilities can be adjusted the next time a similar object is encountered. Fuzzy logic is already in use today in software such as computer speech and handwriting recognition programs, which learn to perform better through "training."

The combination of fuzzy logic and neural networks enables robot pioneers to detect the obstacles in an unfamiliar terrain (left, a sequence of one image being processed), assess the relative safety of various alternative routes, and plot a path to its destination (right, a three-image panorama), all without real-time human guidance.

Neural networks also have the ability to learn from experience. This shouldn't be too surprising, since the design of neural networks mimics the way brain cells -- called "neurons" -- process information.

"Neural networks allow you to associate general input to a specific output," Ayanna says. "When someone sees four legs and hears a bark (the input), their experience lets them know it is a dog (the output)." This feature of neural networks will allow a robot pioneer to choose behaviours based on the general features of its surroundings, much like humans do.