Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Have you ever got stuck in a video game?
Or found a glitch that would prevent you from finishing it?
As many of you know, most well-known computer games undergo a ton of playtesting, an important
step that is supposed to unveil these issues.
So how is it possible that all these bugs and glitches still make it the final product?
Why did the creators not find these issues?
Well, you see, playtesting is often done by humans.
That sounds like a good thing, and it often is.
But, here comes the problem - whenever we change something in the game, our changes
may also have unintended consequences somewhere else away from where we applied them.
New oversights may appear elsewhere, for instance, moving a platform may make the level more
playable, however, also, this might happen.
The player may now be able to enter a part of the level that shouldn’t be accessible,
or, be more likely to encounter a collision bug and get stuck.
Unfortunately, all this means that it’s not enough to just test what we have changed,
but we have to retest the whole level, or maybe the whole game itself.
For every single change, no matter how small.
That not only takes a ton of time and effort, but is often flat out impractical.
So what is the solution?
Apparently, a proper solution would require asking tons of curious humans to test the
game.
But wait a second.
We already have curious learning-based algorithms.
Can we use them for playtesting?
That sounds amazing!
Well, yes, until we try it.
You see, here is an automated agent, but a naive one trying to explore the level.
Unfortunately, it seems to have missed half the map!
Well, that’s not the rigorous testing we are looking for, is it?
Let’s see what this new AI offers.
Can it do any better?
Oh my, now we’re talking!
The new technique was able to explore not less than 50%, but a whopping 95% of the map.
Excellent.
But we are experienced Fellow Scholars over here, so of course, we have some questions.
So, apparently this one has great coverage, so it can cruise around, great, but our question
is, can these AI agents really find game-breaking issues?
Well, look, it just found a bug where it could climb to the top of the platform without having
to use the elevator.
It can also build a graph that describes which parts of the level are accessible and through
what path.
Look!
This visualization tells us about the earlier issue where one could climb the wall through
an unintentional issue, and, after the level designer supposedly fixed it by adjusting
the steepness of the wall, let’s see the new path.
Yes, now it could only get up there by using the elevator.
That is the intended way to traverse the level.
Excellent!
And it gets better, it can even tell us the trajectories that enabled it to leave the
map so we know exactly what issues we need to fix without having to look through hours
and hours of video footage.
And, whenever we applied the fixes, we can easily unleash another bunch of these AIs
to search every nook and cranny, and try these crazy strategies, even ones that don’t make
any sense, but appear to work well.
So, how long does this take?
Well, the new method can explore half the map in approximately an hour or two, can explore
90% of the map in about 28 hours, and if we give it a couple more days, it goes up to
about 95%.
That is quite a bit, so we don’t get immediate feedback as soon as we change something, since
this method is geared towards curiosity and not efficiency.
Note that this is just the first crack at the problem, and I would not be surprised
if just one more paper down the line, this would take about an hour, and two more papers
down the line, it might even be done in a matter of minutes.
What a time to be alive!
Thanks for watching and for your generous support, and I'll see you next time!