Types of Learning

Designers and programmers may be skilled at developing a product, but they can’t predict every problem that will occur. They can’t make a solution to every problem, so they use different types of machine learning for artificial intelligence to make a solution that can be implemented into the game. To gain a better understanding here are the 3 types of machine learning:

1. Unsupervised Learning is feeding data to a system and applying an algorithm to make observations from the data. In this type of learning the AI will help find patterns in the data to make decisions (Coursera, 2022). This type of learning helps make observations and decisions. Without having an expected outcome the AI is free to make any decisions and solutions to the problems presented. An example of this in research is using player data in an algorithm to understand how players perform in the game. (Drachen, 2009)
2. Supervised Learning is feeding data into a system with an expected outcome in mind. In this type of learning the data being fed is made to produce a specific output. In this instance, the AI is being assisted in producing solutions with the labeled data (Coursera, 2023). More specifically the data you put in will have a corresponding output and you want the AI to learn the relationship between the two. In video games, the goal is to have the AI reproduce player behavior in any situation from the data given to it.
3. Reinforcement Learning is when the algorithm or agent can interact with its environment and make a negative or positive reward for the behavior based on the context of the objective (Coursera, 2022). In this type of learning, the AI is learning closest to how the human brain would learn. In game design, this would be good for testing different mechanics and specific missions to see how a player would progress.

Now that the types of learning are understood, one can see that they have many applications in different fields. But, they are also used in other game genres for different purposes. It’s crucial to recognize that machine learning’s role in game design is not a one-size-fits-all approach. Each type of learning—Unsupervised, Supervised, and Reinforcement—serves unique functions and is best suited for particular challenges within the gaming landscape. Whether it’s predicting player behavior, enhancing game mechanics, or understanding player data patterns, machine learning offers innovative solutions to complex issues. Therefore, choosing the appropriate type of machine learning can significantly influence the effectiveness of the game’s design and its ultimate success in engaging players. By leveraging these distinct approaches, designers and programmers can create more dynamic, responsive, and captivating gaming experiences.