Artificial intelligence; Evolutionary Algorithms Solve problems with Random Explorat

[Nak Khid]

https://www.quantamagazine.org/computers-evolve-a-new-path-toward-human-intelligence-20191106/?utm_source=pocket-newtab

 

Quanta Magazine
Computers Evolve a New Path Toward Human Intelligence

Neural networks that borrow strategies from biology are making profound leaps in their abilities. Is ignoring a goal the best way to make truly intelligent machines?

In one test, they placed virtual wheeled robots in a maze and evolved the algorithms controlling them, hoping one would find a path to the exit. They ran the evolution from scratch 40 times. A comparison program, in which robots were selected for how close (as the crow flies) they came to the exit, evolved a winning robot only 3 out of 40 times. Novelty search, which completely ignored how close each bot was to the exit, succeeded 39 times. It worked because the bots managed to avoid dead ends. Rather than facing the exit and beating their heads against the wall, they explored unfamiliar territory, found workarounds, and won by accident. “Novelty search is important because it turned everything on its head,” said Julian Togelius, a computer scientist at New York University, “and basically asked what happens when we don’t have an objective.”
 

The steppingstone principle is a way to inject creativity into artificial intelligence.

The steppingstone principle goes beyond traditional evolutionary approaches. Instead of optimizing for a specific goal, it embraces creative exploration of all possible solutions. By doing so, it has paid off with groundbreaking results.