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Artificial life forms evolve basic intelligence
User ID: 1061233
08/08/2010 09:04 PM
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FOR generations, the Avidians have been cloning themselves quietly in a box. They're not perfect, but most of their mutations go unnoticed. Then something remarkable happens. One steps forward, and that changes everything. Tens of thousands of generations down the line, some of its descendents will evolve memory.
Avidians are not microbes, or sci-fi alien life forms. They are the digital offspring of Charles Ofria and colleagues at Michigan State University (MSU) in East Lansing. They "live" in a computer world called Avida, and replicate using strings of coded computer instructions instead of DNA. But in many ways they are similar to real life: they compete with each other for resources, replicate, mutate, and evolve. They - or things like them - might eventually evolve to become artificially intelligent life forms.
Similar to microbes, Avidians take up very little space, have short generation times, and can evolve new traits to out-compete their rivals. Unlike microbes, their evolution can be stopped at any time, reversed, repeated, and the precise sequence of mutations that led to the new trait can be dissected. "They're wonderful evolutionary pets," says Ben Kerr, a biologist at the University of Washington in Seattle.
They could become so much more. At the 12th annual international conference on artificial life in Odense, Denmark, this month, philosopher and computer scientist Robert Pennock of MSU will present the findings of experiments in which Avidians were made to evolve memory.
"The big question is: how did we get here? Our intelligence didn't evolve all at once," says Pennock. "You need certain ingredients. Memory is one."
Experiments in Avida nearly always start with the simplest possible organisms, ones that can only clone themselves. To make them evolve, the experimenters release them into a competitive environment where the prize is an amount of "food" - aka processing time - which allows organisms to produce more clones.
In early memory experiments, Laura Grabowski, now at the University of Texas-Pan American, Edinburg, set up a food gradient in a computer environment made of a grid of cells. First-generation Avidians were placed at the low end of the gradient, in a cell that had minimal food. Straight ahead of them, however, lay a cell that had more.
The Avidians replicated themselves for nearly 100 generations, "living" and "dying" in the cell. Then one evolved a computer instruction to move forward. When it landed in an energy-richer cell, it reproduced more rapidly. Many thousands of generations later, some of its descendents were seen following the food gradient to its source, where concentrations were highest (Artificial Life 2009, p 92).
Even then the Avidians did not home in on the source. They stumbled their way along the gradient in zigzags, sensing the food and eventually reaching the source. They had evolved to ability to compare food in its current and past locations. "Doing this requires some rudimentary intelligence," says Pennock. "You have to be able to assess your situation, realise you're not going in the right direction, reorient, and then reassess."
Next, Grabowski sent a fresh batch of non-evolved Avidians on a treasure hunt. This time, cells contained a numerical code, which indicated in what direction the organisms should turn to find more food. But there was an additional twist to the task. Some cells contained the instruction "repeat what you did last time". The Avidians once more evolved into forms that could interpret and execute the instruction. "The environment sets up selective pressures so organisms are forced to come up with some kind of memory use - which is in fact what they do," says Grabowski.
This is not unlike evolution in living creatures, and the findings of the MSU computer scientists have attracted interest from biologists. "Laura's work suggests that the evolution of an ability to solve simple navigational problems depends on first evolving a simple short-term memory - and this in digital organisms that still don't exhibit something you would call learning," says Fred Dyer, an MSU zoologist who advised Grabowski. Dyer says this sort of insight would be all but impossible to obtain by studying biological systems.
[link to www.newscientist.com]