Mario may be super, but even he must get bored
hurdling the same Goombas and falling off
the same cliffs over and over.
Fortunately, A new artificial intelligence algorithm
for what's called procedural content generation can
endlessly produce new levels—and
make sure they meet certain criteria.
Video games such as No Man’s Sky
use procedural content generation
to automatically generate up to 18 quintillion
unique planets for players exploring the galaxy,
a daunting task for any human designer.
But programmers still need to hand-craft the rules
that tell the computer how to create such content.
Game levels are particularly tricky to generate
because small changes can make them unplayable -
a stray wall can seal off a critical passage
—but machine learning, an AI technique
by which computers learn from many examples,
has generated levels for several games, including
Super Mario Bros, Starcraft II, and The Legend of Zelda.
In this example of an AI making Mario levels
there are two phases.
In the first, a “generative adversarial network”
learns through trial and error
to transform strings of numbers into levels
indistinguishable from human-created levels.
A second phase then helps find number strings
that lead to levels that are not just realistic
but that fit certain requirements—
such as having a lot of enemies or jumps,
giving researchers precise control over difficulty.
The researchers believe their approach
would work for other games, too.
Another method uses adversarial networks to
produce new maps for Doom,
the classic first-person shooter.
The algorithm creates Doom maps that match
human-created ones visually and on certain
higher-level features, such as
the balance of large and small rooms,
Procedural content generation doesn’t
just save designers time and Mario from tedium.
It could also improve video games --
allowing games to adapt to players on the fly,
so each level is not too hard, not too easy.
Just super.
