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several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation; however, it also introduces new questions: how to compare behavior?

In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework.

The results show that fostering behavioral diversity substantially improves the evolutionary process in the investigated experiments, regardless of genotype or task.

Neural networks -- collections of artificial neurons or nodes set up to behave like the neurons in the brain -- can be trained to carry out a variety of tasks, often having something to do with pattern or sequence recognition. As such, they have shown great promise in image recognition systems. Now, research coming out of the University of Hong Kong has shown that neural networks can hear as well as see. A neural network there has learned the features of sound, classifying songs into specific genres with 87 percent accuracy.

Similar networks based on auditory cortexes have been rewired for vision, so it would appear these kinds of neural networks are quite flexible in their functions. As such, it seems they could potentially be applied to all sorts of perceptual tasks in artificial intelligence systems, the possibilities of which have only begun to be explored.