A computer
science team at The University of Texas at Austin has found that robots evolve
more quickly and efficiently after a virtual mass extinction modeled after
real-life disasters such as the one that killed off the dinosaurs. Beyond its
implications for artificial intelligence, the research supports the idea that
mass extinctions actually speed up evolution by unleashing new creativity in
adaptations. Researchers found that, at least with robots, this is the case.
For years, computer scientists have used computer algorithms inspired by
evolution to train simulated robot brains, called neural networks, to improve
at a task from one generation to the next. The UT Austin team's innovation in
the latest research was in examining how mass destruction could aid in
computational evolution. In computer simulations, they connected neural networks
to simulated robotic legs with the goal of evolving a robot that could walk
smoothly and stably.
As with real
evolution, random mutations were introduced through the computational evolution
process. The scientists created many different niches so that a wide range of
novel features and abilities would come about. After hundreds of generations, a
wide range of robotic behaviors had evolved to fill these niches, many of which
were not directly useful for walking. Then the researchers randomly killed off
the robots in 90 percent of the niches, mimicking a mass extinction. After
several such cycles of evolution and extinction, they discovered that the
lineages that survived were the most evolvable and, therefore, had the greatest
potential to produce new behaviors. Not only that, but overall, better
solutions to the task of walking were evolved in simulations with mass
extinctions, compared with simulations without them. Practical applications of
the research could include the development of robots that can better overcome
obstacles and human-like game agents.
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