Swarm intelligence – where the behavior of many semi-intelligent individuals becomes intelligent in collective activity – think of ants or bees, has been an area of study for some time but on no perceivable schedule or cycle seems to appear in the popular media as a matter of considerable importance. I thought of this while reading a new example, an article in The Economist, Riders on a swarm. The central point of the article is that swarm intelligence points to ways in which human intelligence may work, and as such may be useful to computer scientists developing artificial intelligence. They do need the help, as the article says, because development of artificial intelligence (at least at the human level) has so far been a bust.

It turns out that research on swarm intelligence has many potential applications that are pursued by a diverse set of interests including the military, space agencies, robotics companies, and nanotechnology research. Why?

As the article emphasizes, whatever ‘intelligence’ is – and we still haven’t really crystallized a definition, much less figured out how to create it artificially – it’s very complicated. Perhaps someday we may untangle all the aspects. Meanwhile, the demand for things, including many of our machines, to work in an organized and ‘intelligent’ fashion has also outstripped the ability to provide for it. It seems like a natural avenue of approach to look at making many simple things operating with simple rules perform that old trick of the whole being greater than the parts – in short, using swarm intelligence.

It’s well worth the time to read (or at least scan) the Wikipedia entry on swarm intelligence. It may be something of a revelation to learn how much this field has expanded in the last decade or two, for example:

- Ant colony optimization (ACO), algorithms that use ant behavior to simulate complex environmental and search problems (also applied to bees)
- Particle swarm optimization (PSO), examines the behavior of communicating particles to solve various geometrical and dynamic problems
- Stochastic diffusion search (SDS), using simple agents to converge to solutions of complex problems

There are other branches of SI (yes, Swarm Intelligence), but you probably get the idea. Swarm intelligence comes in many variations, and it’s useful. Some of the research is abstract in the extreme, beloved mostly by computer scientists and mathematicians. Some of the research is driven by immediate practical needs, such as controlling a swarm of nano-robots. From time to time an application or a variant model of swarm intelligence pops into view, but usually it represents yet another gradual advance of a field that continually experiments with the border between simple and complex. And that’s where the Economist article finds the value in applying SI to AI.

But anyone who is really interested in the question of artificial intelligence cannot help but go back to the human mind and wonder what is going on there—and there are those who think that, far from being an illusion of intelligence, what Dr Dorigo and his fellows have stumbled across may be a good analogue of the process that underlies the real thing.

For example, according to Vito Trianni of the Institute of Cognitive Sciences and Technologies, in Rome, the way bees select nesting sites is strikingly like what happens in the brain. Scout bees explore an area in search of suitable sites. When they discover a good location, they return to the nest and perform a waggle dance (similar to the one used to indicate patches of nectar-rich flowers) to recruit other scouts. The higher the perceived quality of the site, the longer the dance and the stronger the recruitment, until enough scouts have been recruited and the rest of the swarm follows. Substitute nerve cells for bees and electric activity for waggle dances, and you have a good description of what happens when a stimulus produces a response in the brain.

[Source: The Economist]