# Decision Tree Learning

`Decision Tree Learning` is an important concept in game development, particularly in the development of artificial intelligence for game characters. It is a kind of machine learning method that is based on using decision tree models to predict or classify information. A decision tree is a flowchart-like model, where each internal node denotes a test on an attribute, each branch represents an outcome of that test, and each leaf node holds a class label (decision made after testing all attributes). By applying decision tree learning models, computer-controlled characters can make decisions based on different conditions or states. They play a key role in creating complex and interactive gameplay experiences, by enabling game characters to adapt to the player's actions and the ever-changing game environment.

Visit the following resources to learn more:

- [@article@Game Strategy - Real Time Decision Tree](https://medium.com/@aleena.sebastian/game-strategy-optimization-using-decision-trees-d4067008eed1)
- [@article@Real Time Decision Tree](https://www.codewithc.com/real-time-decision-trees-in-pygame-ai/)
- [@video@Decision Trees - A Friendly Introduction](https://www.youtube.com/watch?v=HkyWAhr9v8g)