There has always been standard anomaly detection in systems where human eyes determine a baseline threshold, and an alert goes off when the threshold is broken. For decades this has been common practice, but with machine learning, we are in a new age of anomaly detection.
Machine learning adds an additional layer of anomaly detection that humans couldn’t possibly ever reach. The amount of data that a machine learning system can process alongside the adaptation that machine learning brings to the table is priceless.
Employing machine learning in video games has made for a vastly better experience for users and developers alike. We’re happy to highlight the best ways machine learning can be used to improve your gaming experience on all sides of the fence.
What is Machine Learning
Machine learning is a set of algorithms that a computer uses to take in data and make adjustments to improve a program’s experience based on what it’s being asked to do. This is commonly referred to as Artificial Intelligence (AI), but machine learning is slightly different from our common AI depictions.
Machine learning is a computer that is absorbing all the information it’s being told to document and then making adjustments to achieve a specific goal that it has been given.
A great example is a computer that is learning chess. It will be told what each chess piece’s restrictions are, and then it is told to win the game. We don’t tell it exactly how to win the game, but the computer will learn what moves it makes that cause it to lose the game and adjust millions of times till it succeeds.
How is Machine Learning Being Used In Games?
Machine learning is being used worldwide in thousands of different applications, from what types of socks people like to wear the most to beating grandmaster’s players in Starcraft.
In video games, machine learning is typically used to improve the player experience to keep player retention and increase new players. This type of machine learning can be seen by finding when players quit the game, bugs that occur in simulations, and much more!
Finding Player Patterns
Using machine learning to find player patterns can be extremely helpful when trying to see what’s keeping players around as well as what is making them stop playing. The computer can determine how often players are playing till they need breaks or stop playing entirely. Then it can learn at what point there is an excessive amount of people quitting or vice versa.
These player patterns and finding anomalies in gameplay can help developers create more situations that keep players coming back and avoid scenarios that make people leave.
Discovering Bugs Before The Community Does
We are at the point in machine learning where you can actually give a computer control over the same systems a human player would and let them test it till the cows come home.
Computers have a leg up on humans that we’re sure most people have thought of. They don’t have to sleep, and computers aren’t limited by actions per minute!
These two facts mean that you can run a computer using machine learning to discover bugs. You can turn the computer on and tell it to bump into every wall and try every gun on every enemy as fast as it can.
A computer utilizing machine learning can run through the game ten times faster than any of your players possibly could and save you from fixing bugs after a player has already used it to dupe 1,000 rare candies or level up to max level in one hour.
Determine Fastest Playthrough or New Methods
Maybe a more fun way to use machine learning is to figure out the maximum capacity of your games.
When you have a computer that can complete a game a hundred times in the amount of time you could test it once means that it can discover new methods to beat your game or determine the fastest playthrough possible.
This type of machine learning can bring more players in. They will try to beat records or test out new strategies to reach the limits of finishing the game.