Summary

In this chapter, we learned that game AI and academic AI have different objectives. Academic AI researchers try to solve real-world problems and develop AI algorithms that compete with human intelligence, with the ultimate goal of replacing humans in complex situations. On the other hand, game AI focuses on building NPCs with limited resources that seem to be intelligent to the player, with the ultimate goal of entertaining them. The objective of AI in games is to provide a challenging opponent that makes the game more fun to play.

We also learned about the different AI techniques that are used in games, such as FSMs, randomness and probability, sensors, input systems, flocking and group behaviors, path following and steering behaviors, AI pathfinding, navigation mesh generation, and behavior trees.

We'll learn how to implement these techniques inside the Unity engine in the following chapters. In the next chapter, we will start with the basics: FSMs.